mind wandering in learning

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Mind wandering and education: from the classroom to online learning

Karl k. szpunar.

1 Department of Psychology, Harvard University, Cambridge, MA, USA

Samuel T. Moulton

2 Harvard Initiative for Learning and Teaching, Harvard University, Cambridge, MA, USA

Daniel L. Schacter

In recent years, cognitive and educational psychologists have become interested in applying principles of cognitive psychology to education. Here, we discuss the importance of understanding the nature and occurrence of mind wandering in the context of classroom and online lectures. In reviewing the relevant literature, we begin by considering early studies that provide important clues about student attentiveness via dependent measures such as physical markers of inattention, note taking, and retention. We then provide a broad overview of studies that have directly measured mind wandering in the classroom and online learning environments. Finally, we conclude by discussing interventions that might be effective at curbing the occurrence of mind wandering in educational settings, and consider various avenues of future research that we believe can shed light on this well-known but little studied phenomenon.

During the past decade, there has been impressive growth in research concerning the cognitive and neural bases of mind wandering, including a rapid expansion of experimental procedures that have rendered the phenomenon tractable for experimental studies, a growing body of reliable findings, and a number of theoretical proposals aimed to account for the phenomena of interest (for reviews, see Smallwood and Schooler, 2006 ; Smallwood, 2013 ). During the same time period, there has been a similarly impressive increase in the application of findings and ideas from cognitive psychology to understanding learning and retention in educational contexts (for recent reviews, see Roediger and Karpicke, 2006 ; Bjork et al., 2013 ; Dunlosky et al., 2013 ). It seems clear that these two domains of research should be highly relevant to one another, because mind wandering and related attention failures are widely recognized to be common in the traditional classroom setting (e.g., Johnstone and Percival, 1976 ; Bligh, 2000 ; Bunce et al., 2010 ) as well as in online education (e.g., Koller, 2011 ; Khan, 2012 ). Perhaps surprisingly, there has been relatively little research linking the two domains; indeed, only a few years ago, Smallwood et al. ( 2007 ) characterized mind wandering as an “underrecognized” influence in educational settings and provided a useful discussion of experimental results and conceptual/theoretical considerations relevant to linking the two domains.

In the past couple of years, systematic research has begun to emerge that focuses on the incidence and nature of mind wandering in both traditional classrooms as well as online learning environments. The primary purpose of the present article is to provide a focused review and discussion of recent research, as well as some lesser known older studies that examine the occurrence and consequences of mind wandering during both classroom and online lectures. In addition, we consider possible interventions for reducing the occurrence of mind wandering in educational settings and conclude by discussing potentially fruitful directions for future research.

Mind wandering during classroom instruction

Within educational settings, the occurrence of mind wandering is perhaps most readily observable within the context of classroom instruction. Indeed, educators have long been concerned about the possible negative impact of mind wandering on student learning (Brown, 1927 ; Lloyd, 1968 ). It is important to note, however, that few studies have directly measured mind wandering in the classroom. Instead, early research made use of measures such as physical markers of inattention, note taking, and retention. Data emerging from these early studies revealed important clues about the nature of student attentiveness over extended periods of study that have helped to guide more recent research on mind wandering in the classroom. In this section, we review and evaluate the basic findings emerging from these early studies, discuss the possible relation of these findings to mind wandering, and highlight direct attempts to measure mind wandering in the classroom. In addition, we assess the influence of possible interventions for reducing the occurrence of student mind wandering.

Observational approaches

In what is often cited as a classic example of student attentiveness in the classroom, Johnstone and Percival ( 1976 ) asked observers to make note of physical signs of inattention, such as diversions in gaze, as students sat through chemistry lectures. The authors found that initial breaks in attention occurred after approximately 10–18 min of class time, and that the frequency of breaks in attention rose to a level of every 3–4 min toward the end of lectures. Indeed, the notion that student attentiveness decreases as a function of time spent in the classroom has strongly influenced research in this area. Nonetheless, it is important to note that physical markers of inattention should be interpreted cautiously (Wilson and Korn, 2007 ). For instance, students who have momentarily directed their gaze away from the lecturer may still be listening to the lecturer, and not necessarily mind wandering; conversely, a focused gaze does not necessarily indicate a focused mind. Importantly, recent studies have drawn stronger links between physical markers of inattention and mind wandering. For example, Smilek et al. ( 2010 ) recently assessed the relation of blinking to mind wandering during a reading task. In that study, students were asked to indicate whether or not they were paying attention to the text in response to a series of auditory tones. The authors found that blinking was more likely to precede moments of inattention than attention, and suggested that blinking might facilitate the decoupling of attention from the immediate environment during instances of mind wandering. Moving forward, additional research is needed to demonstrate how physical markers of inattention relate to the occurrence of mind wandering in the classroom (for relevant discussion, see Bligh, 2000 ; Rosengrant et al., 2011 ).

Note taking and retention

Various attempts have been made to circumscribe the difficulties associated with inferring student attentiveness via direct observation. For instance, some researchers have focused on note taking. Although note-taking behavior does not necessarily correlate with comprehension (e.g., McClendon, 1958 ), reductions in note taking over time may indicate inattention on the part of students. Unfortunately, the conclusions that can be drawn on the basis of relevant data are equivocal. For instance, Maddox and Hoole ( 1975 ) and Scerbo et al. ( 1992 ) examined the percentage of ideal notes (notes deemed important by the experimenter) that students recorded during lectures (for further discussion on research approaches to note taking, see Aiken et al., 1975 ). Maddox and Hoole ( 1975 ) found no decline in note taking across five 10-min intervals of a geography lecture—44, 54, 50, 52, and 55% of ideal notes. Conversely, Scerbo et al. ( 1992 ) observed a steep decline in note taking across three 12-min intervals of a psychology lecture—97, 67, and 50% of ideal notes (see also Hartley and Cameron, 1967 ; Locke, 1977 ). One possibility for this discrepancy may be related to factors such as student interest. For instance, students in the geography class (51%) took significantly fewer notes across the entire lecture than students in the psychology class (71%), and high levels of initial note taking may be necessary to observe subsequent declines over time. Moreover, additional studies are needed to demonstrate the extent to which inattention and declines in note taking co-occur. Along these lines, Lindquist and McLean ( 2011 ) recently demonstrated that frequent bouts of mind wandering—as measured by direct probes of attention—were associated with lower subjective ratings of note taking. Whether this observation extends beyond subjective reports of note taking to actual note taking behavior remains to be tested.

Alternatively, various researchers have looked to measures of retention as proxies for student attentiveness in the classroom. Specifically, if students are less likely to pay attention to the latter portion of a lecture, then information presented toward the end of the lecture should not be retained as well as information presented in earlier portions of the lecture. Again, the evidence is somewhat mixed. While some studies have found reduced memory for information presented at the end of lectures (Burns, 1985 ), others have not (Thomas, 1972 ; Scerbo et al., 1992 for additional discussion, see McLeish, 1968 ). One possibility for this unreliable pattern of data is that the critical test is commonly presented immediately after the lecture. This design feature may allow students to rehearse information from the final portion of the lecture until the test is administered (Glanzer and Kunitz, 1966 ). In order to more accurately assess what information students have integrated into their knowledge base, additional studies ensuring that students express their understanding of lecture content on the sole basis of long-term memory are needed. In addition to possible primacy and recency effects (e.g., Jersild, 1929 ; Ehrensberger, 1945 ), future studies might also consider the possible influence of other factors that might moderate attention over extended periods of time, such as the distinctiveness or relation of materials to one another across an entire lecture.

Although little is known about the relation of the occurrence of mind wandering and retention of lecture content, Lindquist and McLean ( 2011 ) showed that the frequency of mind wandering in response to direct probes of attention during one lecture was negatively correlated with retention of course material on an exam taken several weeks later. Moving forward, it will be important to more closely investigate the extent to which mind wandering accounts for both the immediate and long-term retention of specific materials from lectures.

Direct probes of attention and mind wandering

We now discuss in more detail studies that have used direct probes of student attention and mind wandering. These studies are important because they provide a more accurate depiction of the extent to which students are actually mind wandering in educational contexts. In one of the initial studies of this sort, Cameron and Giuntoli ( 1972 ) randomly interrupted college lectures with a bell and asked students various questions about the content of their conscious mind, including whether or not they were listening to the speaker, and, if so, whether their listening was “a superficial kind of listening accompanied by frequent distractions,” “a close following of the speaker's train of thought,” or a kind of listening in which they felt that they were “actively meeting the speaker's mind.” Depending on how one classifies students' responses, the results revealed that only between 40 and 46% of students were paying “good attention” to the lecturer or lecture content at any given moment. Using a similar method of consciousness sampling in undergraduate and graduate classrooms, Schoen ( 1970 ) estimated attention during lectures at only 67%, whereas attention during discussion was estimated at 75% (see also Geerligs, 1995 ) and attention during problem solving was at 83%.

Stuart and Rutherford ( 1978 ) asked medical students in twelve 50-min hematology lectures to indicate the extent to which they were paying attention using a 9-point scale (1 = not concentrating at all; 9 = maximum concentration). A buzzer that was audible to students sounded the attention probes at 5-min intervals. The authors found that students, on the whole, never indicated more than an “average level of concentration” throughout the lecture. Interestingly, the authors also found that student attention peaked around 10–15 min into the lecture, but that their attention waned considerably thereafter (see also, Johnstone and Percival, 1976 ; for possible alternative interpretations, see Wilson and Korn, 2007 ).

In a more recent study, Lindquist and McLean ( 2011 ) more directly assessed the occurrence of mind wandering during lectures. Specifically, the authors asked students in three 50-min psychology lectures to report on the occurrence of task unrelated thoughts in response to auditory attention probes that were sounded on five separate occasions—8, 15, 25, 34, and 40 min. Across the entire lecture, task unrelated thoughts were reported in response to ~33% of the attention probes. Moreover, the authors found that task unrelated thoughts were more likely to be reported at the end of the lecture (44%) than the beginning of the lecture (25%). As discussed earlier, Lindquist and McLean also demonstrated a negative impact of mind wandering on note taking and retention. We will revisit this important feature of the authors' data in the context of learning from online lectures, where researchers have greater control over study materials.

Other researchers have used experience sampling paradigms to estimate student attention in everyday life, and such results help contextualize the findings from classroom environments. Unsworth et al. ( 2012 ) asked students to record in a diary their attentional failures during everyday life, and found that the most frequent failures were distraction while studying and mind wandering in class; moreover, 76% of the reported lapses of attention—distraction, mind wandering, or absent-mindedness—occurred in classroom or study situations. Kane et al. ( 2007 ) asked undergraduates to report whether their minds were wandering at random times during the day. On the average, students' minds were wandering 30% of the time (see also, Hurlburt, 1979 ). Furthermore, mind wandering increased when students reported they were tired, stressed, and in boring or unpleasant activities. McVay et al. ( 2009 ) measured mind wandering in the everyday lives of college students, who similarly reported mind wandering on 30% of the samples. Here again, mind wandering was more frequent when students reported feeling tired or anxious, or when they rated the current activity as stressful or boring. Interestingly, mind wandering was also less frequent when participants reported being happy (see also, Killingsworth and Gilbert, 2010 ), good at the current activity (see also Moneta and Csikszentmihalyi, 1996 ), liking the current activity, or rating it as important.

It is important to note that assessments of mind wandering in different contexts are complicated in several important ways. For instance, educational activities such as sitting through a lecture and studying for an exam typically require sustained attentional focus, whereas non-educational everyday activities such as eating breakfast or checking the mail do not necessarily require an individual's undivided attention. Moreover, the consequences of mind wandering also depend on context: The cost of attentional failures during the attention-demanding tasks of education are almost certainly greater than the cost of attentional failures during highly rehearsed, largely automatic tasks of everyday life. As a result, mental experiences such as thinking about a recent or upcoming personal experience may be classified as mind wandering in one context but not the other, and may impact performance in one context but not the other.

In sum, studies making use of direct measures of student attention in educational settings have demonstrated that students frequently report lapses of attention and mind wandering in the classroom, mind wandering appears to increase as a function of time spent in class, and mind wandering may be especially prevalent in educational, as compared to non-educational, settings. Taken together, studies of student mind wandering in the classroom highlight the need for evidence-based research that considers the manner in which classroom instruction is structured, and what interventions might be effective for holding student interest and attention.

Classroom interventions

Educational guidelines commonly urge teachers to intersperse their lectures with tasks that can help to re-focus student attention (e.g., Myers and Jones, 1993 ; Middendorf and Kalish, 1996 ; see also, Olmsted, 1999 ). Unfortunately, only a few attempts have been made to test the effectiveness of such techniques, and the data are often difficult to interpret.

For instance, Burke and Ray ( 2008 ) tested the efficacy of four active learning interventions (student-generated questions, guided reciprocal peer questioning, truth statements, and think-pair-share) across four instructional theory lectures. Each lecture was devoted to testing one of the four interventions, with the intervention occurring halfway through lecture. During each lecture, students were asked to rate their concentration levels on five separate occasions using a 4-point rating scale (1 = not concentrating at all; 4 = fully concentrating), including once at the start of class and once after the intervention. Although the authors demonstrated enhanced levels of concentration following some interventions (student-generated questions) and not others (truth statements), there was no baseline condition against which these effects could be evaluated. Additionally, the order in which students encountered the interventions was not counterbalanced (see also, Young et al., 2009 ). As a result, it is difficult to know for certain how effective the various interventions were in focusing the attention of students.

More recently, Bunce et al. ( 2010 ) asked students in three 50-min chemistry lectures to use clicker technology to indicate whenever their attention to lecture content had been drawn away by various distractions (e.g., texting, completing homework from other courses). In addition, the authors noted various pedagogical techniques used by the instructors of these lectures (e.g., lecturing, quizzing, demonstrations). Although the implementation of the pedagogical techniques was not experimentally manipulated, the authors found that bouts of distraction during lectures were reduced following quizzes and demonstrations. It is also important to note that attentiveness to lecture content was measured via self-reports of distraction that are potentially limited because students are often unaware that they are mind wandering (Smallwood and Schooler, 2006 ; but see recent neuroimaging data suggesting common neural correlates for subjective and objective reports of mind wandering; Smallwood et al., 2008 ). Nonetheless, the results of this study are informative, and additional studies that carefully manipulate that frequency and timing of active learning interventions in the classroom, and that assess distraction and mind wandering in a more direct or objective manner, will be of considerable importance.

Next, we delve into the world of online education, and consider the limitations that mind wandering places on effective learning of lecture videos. As discussed below, the advent of online learning is of great interest in its own right in light of its recent prominence on the educational scene. Moreover, using online lectures as target materials has made it possible to study the occurrence of mind wandering during lectures, and explore possible interventions for reducing mind wandering, with tighter experimental control than is typically available in the classroom.

Mind wandering during online lectures

The studies discussed in the preceding section indicate that mind wandering occurs frequently in the classroom and while studying. As noted earlier, in recent years there has been rapidly growing interest in online education. While online education has existed in some form for nearly as long as the Internet has been around, the emergence of such online platforms as Coursera and edX, which are composed of leading research universities, has led to a dramatic increase in the number of students participating in the entity known as a MOOC or massive open online course. The primary form of instruction in a MOOC is a videorecorded lecture delivered online. Given the frequent occurrence of mind wandering in the traditional classroom, an important question concerns whether mind wandering occurs to a similar, greater, or lesser extent in online settings. While there is very little systematic research on the topic, relevant data have been provided by two recent studies in which participants viewed videorecorded classroom lectures that to some degree resemble those used in online courses. Importantly, by mimicking the online experience in the laboratory, researchers have been able to bring the lecture learning experience, measures of the occurrence of mind wandering during lectures, and tests of possible interventions to ward off mind wandering during lectures under greater experimental control.

Risko et al. ( 2012 ) reported two experiments in which students watched videorecorded lectures—alone in Experiment 1, and with other students in a classroom setting in Experiment 2. Risko and colleagues showed participants one of three 1-h lectures on different topics (psychology, economics, or classics). In Experiment 1, 60 undergraduates watched the lectures and were probed at four different times into a lecture—5, 25, 40, and 55 min. During each probe, students were asked if they were mind wandering at that moment. Overall, participants indicated that they were wandering in response to 43% of the probes, with significantly more mind wandering observed in response to the two probes given during the second half of the lecture (52%) than to those given during the first half (35%). The increase in mind wandering across the lecture was associated with poorer performance on a test of lecture material given shortly after the lecture: students responded correctly to 57% of questions concerning the second half of the lecture, compared with 71% correct responses to questions concerning the first half of the lecture. Further, there was a significant negative correlation between test performance and mind wandering ( r = −0.32): individuals who performed more poorly on the test reported more mind wandering. Experiment 2 yielded a highly similar pattern of results: students reported mind wandering in response to 39% of probes, reports of mind wandering increased significantly from the first half of the lecture (30%) to the second (49%), and mind wandering during the second half of the lecture was associated with significantly poorer test performance compared with the first half of the lecture (for similar results, see Risko et al., 2013 ).

The incidence of mind wandering during videorecorded lectures was notably high—at least as high as the rate of mind wandering during classroom lectures reported by Lindquist and McLean ( 2011 ). One possible contributing factor is the 1-h length of the videorecorded lectures used by Risko et al. ( 2012 ). Some advocates of online education, such as Salman Khan, founder of the highly successful Khan Academy, and Daphne Koller, co-founder of Coursera at Stanford University, have argued that online lectures should be brief—as short as 10 min—in part because of concerns raised by earlier studies of classroom lectures, as discussed above, showing that individuals cannot sustain attention for longer periods of time (Koller, 2011 ; Khan, 2012 ; for possible limitations associated with this view, see Wilson and Korn, 2007 ). Thus, it is possible that mind wandering would occur much less often during videorecorded lectures that are considerably shorter than the 1-h lectures used in the Risko et al. ( 2012 ) study.

Szpunar et al. ( 2013 ) addressed this issue in a study that used a 21-min videorecorded lecture. This study also examined the critical and as yet unaddressed question of whether it is possible to reduce mind wandering during an online lecture. Szpunar et al. ( 2013 ) addressed the question by interpolating brief tests within the lecture. Previous research using materials such as word lists, face-name pairs, and prose passages has shown that interpolating brief tests at regular intervals between lists of stimuli can help to improve retention of materials from the end of extended study sequences (see Szpunar et al., 2008 ; Weinstein et al., 2011 ; Wissman et al., 2011 ).

Szpunar et al. ( 2013 ) reported two experiments in which participants watched a 21-min videorecorded statistics lecture (results of the two experiments were very similar; here we focus on Experiment 2). The lecture was divided into four segments of equal length. Prior to the lecture, all participants were instructed that they might or might not be tested after each segment, and that they would also receive a final test at the conclusion of the lecture. Participants were encouraged to take notes during the lecture. After each lecture segment, all participants completed arithmetic problems unrelated to the lecture for about a minute. However, there were three different groups, which were defined by what the participants did next: the tested group received brief tests on each segment that took about 2 min each; the non-tested group did not receive a test until after the final segment, and continued to work on arithmetic problems for an additional 2 min for each of the segments preceding the final segment; and the re-study group did not receive a test until after the final segment, and was shown, but not tested on, the same material as the tested group for 2 min for each of the segments preceding the final segment. At random times during the lectures, participants in all groups were probed about whether they were paying attention to the lecture or mind wandering off to other topics.

Participants in the non-tested and re-study groups indicated that they were mind wandering in response to about 40% of the probes, but the incidence of mind wandering was cut in to half, to about 20%, in the tested group. Moreover, participants in the tested group took significantly more notes during the lectures (three times as many), and retained significantly more information from the final segment of the lecture, than did than participants in the other two groups, who performed similarly. Participants in the tested group were also less anxious about a final test that followed the lecture and performed significantly better on that final test than those in the other groups. These results indicate that part of the value of testing comes from encouraging people to sustain attention to a lecture in a way that discourages task-irrelevant activities such as mind wandering and encourages task-relevant activities such as note taking.

Taken together, the results of the studies by Risko et al. ( 2012 , 2013 ) and Szpunar et al. ( 2013 ) suggest that mind wandering occurs frequently during the viewing of online lectures regardless of lecture length: both studies found evidence of mind wandering in response to about 40% of probes in non-tested conditions, even though the lectures used by Risko et al. were three times as long as those used by Szpunar et al. We think that these estimates of mind wandering are probably conservative when one considers the conditions that characterize online learning in everyday life: many students may view online lectures under conditions conducive to mind wandering and distraction, such as at home or in dorm rooms that are full of potentially attention-diverting material such as friends, television, Facebook, e-mail, and the like (for further discussion, see Risko et al., 2013 ).

It is encouraging that interpolated testing can dramatically reduce the incidence of mind wandering, and increase the incidence of task-relevant activities such as note taking. Such findings provide some confirmation for those practitioners of online learning who are already incorporating interpolated testing into their online lectures. Nonetheless, the results reported by Szpunar et al. ( 2013 ) must be treated with some caution, both because they were obtained only with a single lecture on a single topic (i.e., statistics), hence raising the question of whether the beneficial effects of testing can be observed across lectures on a variety of topics, and also because it is unclear whether the benefits of testing will persist across multiple lectures. For example, it is possible that students become less responsive to interpolated testing as an online course goes on (Dyson, 2008 ). Given the paucity of data available concerning processes and variables that affect learning from online lectures, these and related questions will be important to address in future studies.

Concluding comments

In sum, early research using proxies of student attention such as physical manifestations of inattentiveness, note taking, and retention, along with more recent studies that more directly probe for instances of mind wandering, highlight the prevalence of attentional lapses and mind wandering in the classroom and during online learning. To some extent, student mind wandering reflects a larger reality of human mental life: just as our minds wander frequently in everyday life, they also wander frequently in educational settings. But mind wandering is particularly relevant to education for two reasons. First, on theoretical and empirical grounds, there is good reason to think that mind wandering is particularly prevalent in educational settings. Online or in the classroom, instruction and studying demand unusually sustained periods of student attention in the presence of unusually salient distractors. In everyday life, one is not typically expected to listen attentively to an hour-long presentation twice a day in a large room full of one's peers, or read large amounts of challenging literature on one's own time instead of socializing or browsing the internet. The attentional demands of lecturing or studying differ from the attentional demands of commuting, cooking, or conversing with colleagues. And as the studies we have summarized (e.g., Unsworth et al., 2012 ) suggest, mind wandering does seem to occur more frequently during instruction and studying than other activities.

Secondly, mind wandering is particularly relevant to education because learning depends critically on attention in ways that other activities do not. Indeed, engaging student attention is often considered an essential feature of education. In a recent survey of nearly 200 Harvard faculty (Advancing the science, 2013), they were asked to complete the following sentence: “For me, an essential of good learning or teaching is _________.” By far, the most common response was “engagement,” and we suspect students, teachers, and educators of all stripes would agree about the central importance of student engagement. Learning experiences—whether they occur in the classroom, library, dining hall, or online—are intended to engage student attention. And for good reason: If a student does not attend consciously to instruction due to an episode of mind wandering, then that student's learning is surely diminished, both for the content not initially encoded and any subsequent content that depends on this initial learning. Thus, because learning is the goal of instruction and studying—and because learning depends on attention—mind wandering presents a particular challenge to education.

What can students or instructors do to reduce unwanted mind wandering during instruction? As we outlined above, there is some preliminary evidence that interspersing periods of instruction with low-stakes quizzing can promote student attention. We also noted earlier that instructors are commonly encouraged to mix up the content of their lectures (Middendorf and Kalish, 1996 ). In fact, cognitive psychologists have demonstrated that interleaving the presentation of various interrelated topics as opposed to dealing with each one in turn can help students to avoid confusing related concepts (e.g., Rohrer, 2012 ). Whether these approaches are effective because frequent changes of topic or brief exposures to any single topic—as compared to prolonged exposure to a single topic—help to sustain students' attention remains an open question for future research. Indeed, education researchers and psychologists have not satisfactorily explored how pedagogy affects mind wandering. To give another example, a considerable amount of research has demonstrated that spacing study over multiple learning sessions as opposed to massing (or cramming) study into a single learning session is a more effective means of ensuring long-term retention of classroom materials (Cepeda et al., 2006 ; Pashler et al., 2007 ; Dunlosky et al., 2013 ) One interesting question for future research may be to examine the extent to which spaced, as compared to massed, study sessions are resistant to bouts of mind wandering and inattention. Given the relative ease of thought sampling methodology and relative importance of student attentiveness, we encourage researchers to expand the empirical literature.

To better understand the causes of and countermeasures against student mind wandering, it is perhaps worthwhile to consider contrasting scenarios. First, how does the experience of attending a lecture differ from the experience of attending other events as an audience member? Indeed, students face attentional requirements during instruction very similar to those of other audiences who passively watch extended presentations. In attending a lecture instead of a movie screening, musical performance, or theatrical performance, however, many of the situational interventions designed to avoid distraction are absent: smartphones and laptop use is allowed (or even encouraged) not banned, lighting is flat instead of focused, the audience whispers, enters, or exits with relative freedom, the stage is bare instead of carefully designed, the presented visuals are often textual, static, or basic instead of graphic, dynamic, and complex, and the audio narration is more likely to be monotonous than lively. For these reasons and others, the conscious experience of watching a 2-h movie is likely very different from that of attending a 2-h lecture.

Other experiments, imagined or real, might be equally revealing. For example, why does the conscious experience of a lecturer differ so greatly from those of the lectured? While students listening to a lecturer wander in their thoughts about a third of the time, the lecturer is typically able to maintain her attention during the same time period and in the same physical space. Why does this simple shift of perspective make such a difference? Might it be the distinction between activity and passivity (e.g., active engagement via intermittent quizzing seems to help), or the asymmetry of the social dynamics between student and instructor? Indeed, recent studies of online learning suggest that asking students to take the perspective of the instructor and teach concepts to virtual students helps to improve retention of course content (Chase et al., 2009 ). Furthermore, perhaps the dramatically different perspective between the lecturer and the lectured furthers the problem of student mind wandering: If the lecture is extremely engaging for the lecturer but less so for students, then this difference of perspective might discourage lecturers from better designing instruction to engage student attention.

Finally, although we have focused considerable attention on the possible pitfalls of mind wandering during classroom and online learning, there also exists the possibility that mind wandering may in some instances benefit the learner. For instance, Baird et al. ( 2012 ) recently demonstrated that the occurrence of mind wandering during a period of incubation was positively correlated with the ability of students to generate solutions to problems designed to test creativity. Under what circumstances might mind wandering benefit classroom or online learning? Do individual differences in the characteristics of mind wandering episodes or propensity to engage in mind wandering predict whether mind wandering might help or hinder learning? Studies designed to answer these and similar questions might not only result in concrete recommendations to students and instruction, but might also uncover new insights into mind wandering, attention, and psychology.

Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

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Review article, benefits of mind wandering for learning in school through its positive effects on creativity.

• Educational Psychology, Department of Psychology, Faculty of Human Sciences, University of Potsdam, Potsdam, Germany

There is broad agreement among researchers to view mind wandering as an obstacle to learning because it draws attention away from learning tasks. Accordingly, empirical findings revealed negative correlations between the frequency of mind wandering during learning and various kinds of learning outcomes (e.g., text retention). However, a few studies have indicated positive effects of mind wandering on creativity in real-world learning environments. The present article reviews these studies and highlights potential benefits of mind wandering for learning mediated through creative processes. Furthermore, we propose various ways to promote useful mind wandering and, at the same time, minimize its negative impact on learning.

Introduction

Mind Wandering (MW) is commonly conceived as a loss of mental focus on a given primary activity in favor of thoughts that are unrelated to this activity ( Smallwood and Schooler, 2006 ; Smallwood and Schooler, 2015 ). For example, while reading a book for school, one may start to think about some event in the past or ruminate about a current problem. This definition implies that shifting one’s mental focus to something other than the current task is likely to be detrimental for task performance. Indeed, a large number of studies have shown reduced task performance due to MW in various domains of cognitive functioning (e.g., Smallwood et al., 2006 ; McVay and Kane, 2009 ; Galéra et al., 2012 ; Unsworth et al., 2012 ; Stawarczyk and D’Argembeau, 2016 ). The range of related observations spans from lower reaction times and more errors in in laboratory tasks, to weaker performance in everyday activities such as safely driving a car in a concentrated manner ( Galéra et al., 2012 ) or actively engaging in a conversation without being distracted ( Unsworth et al., 2012 ). Most relevant to this article is the observation that particularly performance in educational contexts seems to be negatively associated with the occurrence of MW (e.g., Dixon and Bortolussi, 2013 ). For example, several studies found negative effects of MW on text comprehension in university students (e.g., Lindquist and McLean, 2011 ; Risko et al., 2012 ; Unsworth and McMillan, 2013 ; Wammes et al., 2016 ; Soemer and Schiefele, 2019 , 2020 ) as well as secondary school students (e.g., Soemer et al., 2019 ). Likewise, MW has been found to affect learning during lectures ( Hollis and Was, 2016 ; Kane et al., 2021 ).

Despite these negative effects of MW on learning, several other studies have found positive effects of MW on learning-related constructs, in particular on creativity (e.g., Baird et al., 2012 ; Agnoli et al., 2018 ). Since creativity is commonly considered as beneficial for learning (e.g., Dollinger, 2011 ; Lee et al., 2014 ; Leopold et al., 2019 ), previous research linking MW to reduced task performance might indeed miss potentially useful aspects of MW for learning that are mediated through creative processes. Thus, in the following, we present a more balanced view on MW highlighting both its well-known detrimental effects and its potential benefits on learning with a particular focus on creativity.

Mind Wandering

According to a common definition ( Smallwood and Schooler, 2015 ), MW is a phenomenon consisting of three characteristics: (1) There is a primary task to be carried out. (2) The mind is losing focus of the primary task and follows, instead, thoughts that are unrelated to the primary task. (3) This shift of attention is self-generated and not triggered by an external stimulus (e.g., acoustic or visual distractors in the environment).

Noteworthy, MW shows some overlap with the phenomenon of “daydreaming” ( Klinger, 2009 ; McMillan et al., 2013 ). Both constructs involve internally generated thoughts that are not cued by an external event. However, in contrast to MW, the definition of daydreaming does not presuppose a concurrent primary task; in other words, daydreaming also includes situations such as having a walk in the park or riding a bus. In an early approach, Singer (1966) identified different styles of daydreaming, one of which was labeled “positive constructive daydreaming.” This style of daydreaming is characterized by playful, wishful imagery, and creative thought. Moreover, positive constructive daydreaming was later related by Singer to the arousal of positive emotions and the efficiency of future plans (see McMillan et al., 2013 ). Singer also identified two less “beneficial” daydreaming styles: “guilty-dysphoric daydreaming” and “poor attentional control,” the latter corresponding to what is typically associated with the term MW nowadays. Importantly, these latter two styles of daydreaming were found to be associated with negative consequences in a wide variety of domains and were not related to creativity (e.g., Huba et al., 1977 , 1981 ). Interestingly, however, contemporary research on the more narrowly defined construct of MW has rarely made a comparable distinction between positive and negative forms of MW, although it is theoretically possible to do so, as we will argue below.

On the other hand, MW researchers have differentiated between forms of MW with regard to their intentionality (i.e., intentional vs. spontaneous MW) and situation-specificity (situation-specific state-level vs. dispositional trait-level MW; e.g., Soemer et al., 2019 ). Spontaneous MW involves an unintentional und uncontrollable shift of an individual’s attention from the primary task to self-generated thoughts, whereas intentional MW is considered to be induced deliberately or at least tolerated whenever it occurs spontaneously (that is, individuals do not try to focus back on the primary task once they notice, see Seli et al., 2016a ). State-level MW means that individuals’ MW occurs only in specific situations but this may be context-induced and not a general trait of an individual. In contrast, trait-level MW refers to the fact that some individuals have a relatively stable (high or low) level of MW when working on various tasks. Accordingly, trait-level MW shows considerable definitional overlap with the aforementioned construct of daydreaming, the former being a slightly more restricted form of the latter.

Mentioning daydreaming in this article, which otherwise focuses specifically on MW, is important for two reasons. First, research on daydreaming illustrates that it has long been suspected that creativity may be related to an individual’s propensity to pursue internally generated mental content (e.g., Singer and Antrobus, 1963 ). Second, the primary focus of daydreaming research on the individual differences level highlights the need for greater differentiation in modern MW research on this topic. This includes identifying commonalities and differences of the above-mentioned subtypes of MW (e.g., state-level vs. trait-level MW) and examining their potentially diverse relationships to other constructs (e.g., for a contrasting effect of state vs. trait level MW on reading comprehension, see Soemer et al., 2019 ), which includes creativity. For convenience reasons, however, we will equate daydreaming with trait-level MW throughout the remainder of this paper.

Creativity and Its Measurement

Creativity has been characterized as the ability to create something novel, unique, or unusual (summarized as “original”) that is considered to be useful, appropriate, or fitting (i.e., efficient; Runco and Jaeger, 2012 ). Individual differences in creativity seem to be strongly connected to individual cognitive characteristics such as intelligence and, in particular, “divergent thinking” ( Guilford, 1967 ). Due to its continuing importance in creativity research, we will address divergent thinking first. However, relying solely on divergent thinking as a measure of creativity has its flaws, as explained below. Therefore, this review introduces another facet of creativity, namely creative problem solving , in order to look on creativity from a different perspective and to offer an alternative way of measuring it. At the end of this chapter, we will elaborate on the creative process.

Divergent Thinking

Divergent thinking is characterized by generating a large number of possible answers to a given problem. In contrast, “convergent thinking” is directed at producing the single best answer to a given problem ( Guilford, 1967 ). Some authors use the term “divergent thinking” synonymously with the term “creativity” (e.g., Frith et al., 2021 ), whereas others consider creativity to be a much broader construct that entails divergent thinking as one of its facets (e.g., Lubart et al., 2013 ; Silvia, 2015 ). Because of its ease of measurement and relevance for creativity, however, divergent thinking has been widely used as the main measure of creativity, as is also the case in most of the research being discussed in the following.

For a proper evaluation of the following studies, it is essential to recognize some of the flaws in the measurement of creativity in the sense of divergent thinking. These flaws originate in the assessment methods that were developed by Wallach and Kogan (1965) based on the work of Guilford (1957) . Notably, Wallach and Kogan (1965) basically equate creativity and divergent thinking. Their test battery relies on evaluating the creativity (or divergent thinking) of individuals in terms of the “uniqueness” of their answers to a given problem. For example, the “unusual uses task” (also known as the “alternative uses task”) requires the participants to name multiple unique unusual uses for different every-day objects, such as a brick or an empty bottle. Each response that is given by no other participant is judged as being “unique” and the answering person is awarded one point. This traditional method of measuring creativity/divergent thinking has been criticized (cf. Silvia, 2015 ) but is used until today (e.g., Baird et al., 2012 ).

There are two main problems with the traditional measure of creativity. First, the traditional measure only accounts for the originality facet of creativity but ignores the efficiency facet ( Runco and Jaeger, 2012 ). For a comprehensive measure of creativity, it is necessary to evaluate if a given answer is not only unique but also appropriate or useful. Second, the uniqueness of each response is inversely related to the number of participants being tested, because each participant increases the chance for a repetition.

In an alternative approach to divergent thinking, Torrance (1966 , 1974 , 2008) addresses the problem of neglecting the efficiency facet of creativity through adding other factors (e.g., fluency and elaboration) to the measurement that also account for appropriateness. The Torrance Tests of Creative Thinking (TTCT; Torrance, 1966 , 1974 , 2008 ) measures divergent thinking using a variety of tasks, such as the unusual uses task. In addition to the creativity facet originality , the TTCT assesses response characteristics such as the amount of relevant details provided (i.e., elaboration ) and the number of interpretable, meaningful, and pertinent responses (i.e., fluency ). Moreover, another factor, flexibility , refers to the number of distinct categories to which relevant responses can be assigned. Noteworthy, all of these dimensions (with the exception of originality) implicitly include a verification of the efficiency facet (e.g., for the dimension fluency: “Are the responded details really pertinent?”). However, the creativity facet efficiency still seems neglected, given its indirect implementation through these dimensions. Originality, in contrast, is a discrete dimension of the TTCT by itself and receives therefore more attention in this approach. More recent approaches to assess creativity/divergent thinking suggest other ways to avoid the problems of the traditional method. On the one hand, Smeekens and Kane (2016) addressed the need for more appropriate task instructions. Accordingly, Beaty et al. (2014b , p. 1189) suggested to ask participants “to come up with something clever, humorous, original, compelling, or interesting.” This type of instruction seems more likely to motivate participants to produce responses that are both original and appropriate, whereas the traditional method emphasized uniqueness ( Torrance, 2008 ) and quantity of answers. On the other hand, researchers have suggested procedures to assess the aspect of appropriateness more directly. This can be accomplished by letting expert raters independently judge the appropriateness (in addition to the originality) of the participants’ responses ( Silvia, 2015 ). As an example, Amabile (1982) has developed the Consensual Assessment Technique (CAT) for rating the creativity of a wide variety of products (see Baer and Kaufman, 2019 ).

Creative Problem Solving

Creative problem solving can be described as the ability to successfully engage in problems whose solution demands to gain a deeper insight into the problem itself by restructuring the mental representation of that problem (cf. Weisberg, 2015 ; He and Wong, 2021 ). In more detail, a first attempt to solve those kind of problems usually fails due to insufficient or hidden information that is needed for solving (i.e., it is a so-called ill-defined problem; DeYoung et al., 2008 ). In order to restructure the initial mental model of the problem one must rephrase the own problem-solving approach by changing the viewpoint on the problem after realizing that the initial approach will not lead to the solution (see Beaty et al., 2014a ). Furthermore, ill-defined problems could be conceived of as the antithesis to well-defined problems. While well-defined problems consist of clear specifications for the three elements of the problem space, namely (a) the problem situation, (b) rules and strategies to solve the problem and (c) the characteristics of the goal state, ill-defined problems are missing at least one of these elements ( Newell and Simon, 1972 ). Tasks that capture creative problem-solving ability through ill-defined problems are referred to as insight problems . One example for a verbal insight problem is “A man in a town married 20 Women in the town. He and the women are still alive, and he has had no divorces. He is not a bigamist and is not a Mormon and yet he broke no law. How is that possible?” (Solution: The man is the minister who married the 20 women to their respective husbands; Weisberg, 2015 , p. 7). In addition to such verbal-only formulated insight problems other insight problems provide additional visual input (e.g., a sketch) that must be processed for solving the problem (e.g., the pigpen problem; Lin et al., 2012 ).

Characteristic of insight problems is only one solution is correct, in contrast to divergent thinking tasks in which a person is supposed to generate as many answers as possible. Moreover, contrary to divergent thinking tasks that overemphasize the originality facet of creativity but neglect the efficiency facet, creative problem solving tasks such as insight problems assess both facets of creativity since there is only one correct answer that is actually original. In conclusion, one needs to compensate for the missing parts of the problem (i.e., the missing specifications of the problem situation, the solving mechanisms and/or the goal state) in order to solve ill-defined problems such as insight problems, whereas misspecification is not a problem in divergent thinking tasks.

The Creative Process

One common approach to conceptualize creative processes is the classic four-stage model of the creative process ( Wallas, 1926 ), which many contemporary models of creativity are based on (e.g., the componential model of creativity, Moriarty and VandenBergh, 1984 ; Amabile, 1996 ). As its name suggests, the classic model divides the creative process into four stages that ultimately lead to a creative output: preparation , incubation , illumination and verification . Although an individual usually proceeds through the stages sequentially one by one, Wallas stated that returning to former stages is possible if the problem to be solved requires this.

According to Wallas (1926) the preparation stage serves to preliminarily analyze, define and set up a given problem using problem-relevant knowledge and analytical skills. Noteworthy, individuals carry out these steps consciously, whereas in the following incubation stage the mind starts to work unconsciously on the problem. This second stage is characterized by taking a break from the problem and turning attention to other subjects. However, while being engaged in something different, the mind is still working on the “old problem” in a hidden way, forming many trains of associations, rejecting most of them as being useless but sometimes encountering a promising idea. When this happens, the next stage, illumination, begins and the formerly hidden idea breaks through into consciousness accompanied by a feeling of sudden enlightenment. The last stage, verification, serves to refine, develop and evaluate the produced idea. This stage proceeds in a fully conscious way again.

The Contributions of Creativity to School Learning

Our assumption of a positive effect of MW on learning through enhanced creativity presupposes a significant relation between creativity and learning. Substantial evidence for this relation has been provided in the past. For example, in their meta-analysis, Gajda et al. (2017) reviewed the data from 120 studies and reported an average correlation between creativity and academic achievement of r = 0.22. The authors identified two influential moderator variables: the type of creativity measure (e.g., self-reports vs. standardized tests) and the type of learning measure (e.g., grade point average vs. subject knowledge tests). It was found that the relation between creativity and learning performance was stronger when creativity was assessed through standardized tests (e.g., the TTCT; Torrance, 1966 , 1974 , 2008 ; Divergent thinking tasks, such as the unusual uses task; Wallach and Kogan, 1965 ) than through self-report scales. On the other hand, they found a weaker association between creativity and learning performance when the latter was measured as grade point average compared to standardized achievement tests. In conclusion, Gajda et al. suggested the development of more precise measurement instruments that are better suited to investigate the nature of this relationship between creativity and learning. In accordance with this suggestion, Karwowski et al. (2020) presented a new instrument to measure both creativity and learning [Creativity and Learning in School Achievement Test (CLISAT)] that particularly differs from other instruments for measuring creativity and learning by using a domain-specific assessment. Accordingly, the CLISAT measures both creativity and learning in a particular school subject related domain, such as math or language, while using school-based material. To give an example for a math task, one task from the test demands students to match the correct grid of a cuboid out of four alternatives with a given three-dimensional illustration of a cube. Accordingly, a creative task in the same domain asks the students to divide different forms into parts of equal size.

While validating psychometric properties (e.g., validity and reliability measures) of the CLISAT on 2,372 students of primary and middle school, Karwowski et al. (2020) used their instrument for a further investigation of the association between creativity and learning. They found that having academic knowledge particularly in math was inductive for creative performance in tasks of the same domain (i.e., math). However, for language they could not find evidence for this association. On the other hand, creativity performance in both math and language-related tasks positively predicted academic performance in tasks of the same domain. Intriguingly, in the case of math, particularly weak task performance was predicted by the creativity measure. Regarding this finding, Karwowski et al. assume that having high creativity skills could particularly be beneficial in generating and testing solutions to easy mathematical problems, since these allow various approaches. In contrast, difficult mathematical tasks would be more limited to be solved by only one correct approach. In the domain of language the domain-specific creativity measure predicted performance in language-related tasks over the whole difficulty range. Given these findings, the authors propose a mutual relationship between creativity and school learning.

Some other work pinpointed the significance of creativity for other domains than general learning. In his review regarding the importance of creativity for mathematics Mann (2006) elaborates over the meaning of an additional promotion of creativity for (gifted) students of mathematics. The author concludes that particularly in mathematics, traditional teaching relying on methods involving demonstration and practice using closed problems with predetermined answers, will rather produce computational experts that lack the ability to use their skills in meaningful ways. Thus, although it may seem counterintuitive at first, mathematics in particular could benefit from having an antithesis (i.e., a creative perspective) to the logical, predefined ways of approaching a problem. In contrast to mathematics, the connection between creativity and writing appears more obvious. For example, there is evidence, that the amount of time spent with reading and writing activities of university students is associated with them showing better creative performances ( Wang, 2012 ). Intriguingly, this study also indicated that just having a positive attitude toward reading and writing activities is connected to better creative performances. Moreover, particularly the writing in foreign languages may be connected to higher creative performances ( Wang, 2012 ; Niño and Páez, 2018 ).

Concludingly, despite the positive evidence for a stronger relation between creativity and school learning, a number of open questions remain. These refer in particular to the unresolved causal nature of the creativity-learning relation. In our present theoretical analysis, however, we regard a reciprocal relationship to be most probable. As has been argued by Gajda et al. (2017) , the process of being creative would ultimately lead to learning outcomes and the process of learning will ultimately result in creative outcomes.

Mind Wandering and Its Relation to Creativity

Mind wandering and divergent thinking.

In one of the first studies directly addressing the relation between MW and creativity, Baird et al. (2012) examined whether MW could account for the well-known enhancement of creative problem solving after a break. In what we will call “incubation paradigm” in the following ( Sio and Ormerod, 2009 ), participants are confronted with a problem they have to solve within a given period of time. In terms of the four-stage model ( Wallas, 1926 ), this confrontation can be classified as the first stage of the creative process, preparation. After expiration of the given time, the participants are offered an intervening break during which they do not process the task. This part constitutes the incubation stage of creative process. When the break is over, the participants continue to process the initial task again. Intriguingly, participants’ ability to come up with sudden intuitive solutions to creative problems is usually found to be improved through this break (i.e., the incubation effect). Furthermore, a recent meta-analysis found the incubation effect to be stronger when the break is filled with a mentally non-demanding task ( Sio and Ormerod, 2009 ). In terms of the four stage model manipulating the incubational stage through providing a non-demanding filler task can be regarded as a kind of enhancement of the idea-generating effect that is typically associated with this phase. Baird et al. (2012) tried to replicate the incubation effect and hypothesized that the better performance after a break may be associated with a higher frequency of MW. According to their hypothesis, being engaged in a non-demanding filler task during the break would increase the likelihood that participants engage in MW (which is consistent with the finding that MW is more frequent in non-demanding relative to demanding tasks; e.g., Smallwood et al., 2003a ; Seli et al., 2018 ). More MW, in turn, would promote creative processes that are associated with creative problem solving. In their study, participants were randomly assigned to one out of four experimental groups. The groups differed in the filler task that participants had to perform during the break and thus, in the demands of the tasks. In particular, participants of one group had to perform a low-demanding reaction-to-a-stimulus task that was expected to maximize MW and thereby promote problem solving during the incubation break. Participants of the other three groups performed a highly demanding n-back task ( Kirchner, 1958 ), no task or had no break at all. After the break, participants were asked to estimate how frequently their minds lost focus from the filler task (i.e., MW frequency). The main task in this study was the unusual uses task ( Wallach and Kogan, 1965 ) that was presented before and after the break. Indeed, the results showed the highest MW rates for participants occupied with the low-demanding task when compared to the participants of all the other groups. Furthermore, participants of the low-demanding task group showed the highest improvements in their amount of responses in the primary task after the incubation break when compared to their performance before the break. Baird et al. (2012) suggest that the higher MW frequency in the low-demand task group may lead to better creative insights (during the incubation break) which, in turn, is reflected in better results on the main task (i.e., the unusual uses task). Noteworthy, thoughts related to the main task did not differ between the groups meaning that these thoughts could not account for performance differences between groups.

While suggesting a close relationship between MW and creative processes, the positive association between MW frequency and creative performance does not necessarily imply a causal relationship between the two constructs, because MW was not directly manipulated between groups. On the other hand, it should be noted, that an experimental manipulation of MW is difficult to achieve (however, for an attempt to experimentally induce MW see McVay and Kane, 2013 ). Additionally, Baird et al. (2012) concluded that an increase in MW frequency during a break facilitates the incubation effect as a single element of the creative process (see also Wallas, 1926 ), but not creative problem solving in general. In addition to that, we argue that the unusual uses task used by Baird et al. is not ideal for measuring creative problem solving due to its neglect of the appropriateness facet of creativity that is best measured with insight problems (cf. He and Wong, 2021 ). Instead, the authors showed a connection between MW and divergent thinking that is technically not a measure of creative problem solving, although it can be considered a component of creativity. Smeekens and Kane (2016) argued that the applied manipulation of the task (i.e., alternating the demands) could certainly explain both the increase in the frequency of MW and also improvements in divergent thinking, in line with prior studies (e.g., Smallwood et al., 2003b ; Sio and Ormerod, 2009 ). Critically, the conclusion that MW causes this increase in divergent thinking would not be compelling based on this experimental design.

Mind Wandering and Its Relation to Creative Problem Solving

Another study supporting the hypothesis that MW relates to creativity was conducted by Tan et al. (2015) . This study likewise utilized the incubation paradigm to trigger creative solutions, while examining participants’ MW activity. However, in contrast to the work of Baird et al. (2012) , this study did not manipulate the filler task; that is, all participants had to perform the same relatively non-demanding version of the “sustained attention response task” (SART; Robertson et al., 1997 ). Furthermore, this study used a different main task as a measure of creativity (i.e., creative problem solving), the number-reduction task ( Wagner et al., 2004 ) that required participants to match numbers and respond in a rule-based fashion by returning another number until the seventh response of each trial was given. Participants were informed that only the seventh response would be scored, while the former responses served to determine this last one. Crucially, there was a hidden mechanism that generated the numbers meaning that the participants were able to shortcut the whole trial; that is, they could simply submit their seventh response number early. Tan et al. (2015) assumed that only those participants who figured out the hidden mechanism were able to reliably submit the correct seventh number early. In addition, participants were asked at the end of the experiment what rules (if any) they applied to determine the seventh response. As a result, participants that discovered the hidden rule had more frequent MW occurrences than participants that did not, while participants of both groups did not differ in various control variables (e.g., working memory capacity, motivation and meta-awareness for MW). These results suggest that the SART is a suitable filler task to improve creative output during the incubation period in addition to the reaction to a stimulus task used by Baird et al. (2012) . Moreover, Tan et al. showed that in addition to divergent thinking also creative problem solving can be promoted through performing a non-demanding task during the incubation stage of creative process. However, given the absence of any experimental manipulation of MW in this study, evidence for a causal relation between MW and creativity is still lacking.

Subtypes of Mind Wandering and Their Relations to Both Components of Creativity

Another important study by Agnoli et al. (2018) supports the hypothesis that MW is positively associated with creativity while extending the findings of Baird et al. (2012) and Tan et al. (2015) in two ways. First, the study succeeded in generalizing previous findings on the relation between MW and creativity to a novel paradigm. Instead of using the incubation paradigm, creativity was assessed both as a trait through a questionnaire that asked participants about accomplishments in 10 different domains of creativity such as creative writing or culinary arts (i.e., Creative Achievement Questionnaire; Carson et al., 2005 ) and by the so-called “titles task” ( Guilford, 1968 ). This task measures divergent thinking by requiring participants to produce multiple alternative titles for widely known movies or books. On the other hand, everyday MW (i.e., trait-level MW) was measured by the Five Facets Mindfulness Questionnaire (FFMQ; Baer et al., 2006 ) and two self-report scales that differentiated between intentional and spontaneous MW (MW-D and MW-S; Carriere et al., 2013 ). One advantage of the Creative Achievement Questionnaire is that it inquires about accomplishments of the past in a standardized way, which makes it largely objective and independent from the ongoing study. Furthermore, the questionnaire measures creativity on a relatively stable trait level, not in a particular situation. This trait-level measure is complemented by the titles task that captures situation-specific creativity (i.e., divergent thinking). A second extension of previous findings consists of differentiating between intentional and spontaneous MW (e.g., Seli et al., 2016b ) and relating these two forms of MW to creativity. Indeed, the authors found different associations between intentional and spontaneous MW, on the one hand, and situation-specific creativity (i.e., divergent thinking), on the other. That is, intentional MW was positively related to their measure of divergent thinking, whereas spontaneous MW was negatively related to divergent thinking. However, they did not succeed in finding a relation between MW and trait-level creativity.

Similarly, to Agnoli et al. (2018) a study from Preiss et al. (2016) showed positive correlations between trait-level MW and measures of creativity. They investigated whether trait-level MW can be associated with both divergent thinking and creative problem solving. Whereas the former was measured with the unusual uses task (corrected for appropriate answers), the latter was measured with a test, in which participants were presented with three words to which they had to find a matching word. Participants had to consider a given rule for the matching of the words. For instance, one rule was to find a word that can be used to produce a meaningful compound word with each of the three presented words (e.g., the response word “stone” for the words “mile,” “age,” and “sand”; see Bowden and Jung-Beeman, 2003 ). Trait-level MW was measured using the Daydreaming Frequency Scale from the Imaginal Processes Inventory (IPI; Singer and Antrobus, 1966/1970 ). The results showed trait-level MW to be a predictor of both creativity measures even when fluid intelligence and a measure of participants’ existing reading problems were taken into account. This result suggests that a differentiation of the MW construct into a state-level and a trait-level form could be useful to further investigate the MW-creativity relationship. However, it should be noted that this study only provides evidence for a correlational association between trait-level MW and measures of creativity, and it did not take into account state-level MW.

Studies That Contradict a Connection Between Mind Wandering and Creativity Measures

In contrast to those studies that found positive evidence for a connection between MW and creativity there are several other studies showing null results (e.g., Smeekens and Kane, 2016 ; Frith et al., 2021 ). Interestingly, a study from Smeekens and Kane (2016) directly addressed the results from Baird et al. (2012) and contrasted them with their own findings. Like Baird et al. (2012) , their study used an incubation paradigm. However, although their study design matched that of Baird et al. (2012) closely, Smeekens and Kane (2016) failed to replicate the results within three relatively similar experiments, one of them being an approximate replication of Baird et al. (2012) study. However, both studies differed in a number of details, because Smeekens and Kane used an online measure of mind wandering compared to a retrospective questionnaire used in Baird et al. (2012) study. Additionally, the study from Smeekens and Kane differed from Baird et al. (2012) study in the instructions given to participants, the assessment of divergent thinking (i.e., a subjective assessment was used) and some minor details. The authors reported that there was no evidence for a positive association between the frequency of MW during an incubation period and an improvement in divergent thinking after that break. These null results were considered by the authors to be more credible than the findings from the study of Baird et al. (2012) because of a number of methodological problems in the latter study, such as measuring MW in a retrospective way that, in their view, may be inaccurate due to memory biases and mental aggregation errors.

Similarly to Smeekens and Kane (2016) and Frith et al. (2021) did not find evidence for a positive relationship between state-level MW and divergent thinking. Their main study goal, however, was to examine whether attentional control can account for the well-known association between fluid intelligence and creativity (see also Silvia, 2015 ). Here, attentional control is defined as “overarching term that incorporates various complex control processes responsible for regulating goal-directed thought and behavior” ( Frith et al., 2021 , p. 2). It was assessed through three laboratory measures of attentional restraint (see McVay and Kane, 2012 ; Kane et al., 2016 ). Furthermore, this study defines MW as being a failure of attentional control. Therefore, an investigation of the effect of mind wandering on divergent thinking was of minor nature. State-level MW was measured by thought probes. Using this setup, Frith et al. did not find a significant relation between MW and divergent thinking when fluid intelligence and attentional control were controlled for.

It should be noted, however, that the study of Frith et al. (2021) used a relatively demanding task during the incubation break, in contrast to the majority of previous studies examining the relation between MW and creativity. As we will argue below, this might be a crucial difference between this study and other studies, because task difficulty is known to affect intentional and spontaneous MW differently (e.g., Seli et al., 2016b ; Soemer and Schiefele, 2019 ). Furthermore, the MW measure was not differentiated (e.g., in its intentionality) and only state-level MW was assessed.

Summary and Evaluation

The existence of a relationship between MW and creativity is a controversial issue based on currently available research. On the one hand, it is theoretically well conceivable that MW has positive impacts on creativity because it consists of self-generated contents (e.g., mental images, elaborations, metacognitive thoughts) that could potentially be important for a task at hand that requires some degree of creativity. In line with this hypothesis, early daydreaming research beginning in the mid of the last century as well as a number of contemporary studies have provided evidence for a positive relationship between two components of creativity—divergent thinking and creative problem solving—and MW (e.g., Singer, 1966 ; Baird et al., 2012 ; Preiss et al., 2016 ; Agnoli et al., 2018 ). On the other hand, some recent studies have reported null results suggesting that the circumstances under which positive associations can be found still need to be examined in more detail (e.g., Smeekens and Kane, 2016 ). In addition, it is well known that MW occurring during task execution can be detrimental to task performance in various domains (e.g., Smallwood et al., 2008 ; Galéra et al., 2012 ; Soemer and Schiefele, 2019 ), so why should this be different for tasks that require creative processes?

Evaluating the results of the above reviewed studies, it appears that the relationship between MW and creativity will not be as simple as stating that MW that occurs during a task requiring creative processing would directly bring improvements for that task. Instead, we propose that one needs to distinguish between different forms of MW and examine whether these forms differ in their relationship with creativity (i.e., whether some of them show more positive or negative correlations than others). Particularly, two meaningful distinctions of MW were suggested in some MW studies: the intentionality of MW (e.g., Forster and Lavie, 2009 ; Carriere et al., 2013 ; Seli et al., 2015a , b ; Agnoli et al., 2018 ; Soemer and Schiefele, 2020 ) and the trait-level vs. state-level distinction (e.g., Preiss et al., 2016 ; Soemer et al., 2019 ). We propose that the omission of such distinctions could at least in part be responsible for the contradictory results of the aforementioned studies.

Regarding the intentionality dimension, there is evidence that intentional and spontaneous MW exert different effects on divergent thinking, an important dimension of creativity. Specifically, the study of Agnoli et al. (2018) suggests that the intentional (but not the spontaneous) form of MW may be positively related to divergent thinking. For this reason, studies examining the relation between MW and creativity are more likely to find supportive evidence if they particularly focus on intentional MW and set up conditions in which intentional MW becomes the dominant form of MW. One factor affecting the balance between intentional and spontaneous MW, for example, are the demands of a task; that is, easy tasks are more likely to shift this balance to intentional MW, whereas difficult tasks are more likely to do the opposite (e.g., Seli et al., 2016b ). For this reason, studies that use a highly demanding filler task for the incubation period are less likely to find evidence for a positive relation between MW and creativity. This may in fact be one of the primary reasons for Frith et al. (2021) failure to demonstrate a positive association between MW and creativity.

Regarding the second meaningful distinction between trait-level and state-level MW, the majority of recent studies has primarily focused on the latter. Indeed, general MW research has highlighted the detrimental effects of state-level MW while carrying out a given primary task, on performance in that task (e.g., Soemer et al., 2019 ), contrary to some studies in the field of creativity. However, one crucial difference here is that studies on MW in other fields (including learning) examined the effects of MW on the same task during which it occurred (e.g., the effect of MW during reading on later comprehension). The incubation paradigm used in many studies on the relation between MW and creativity, in contrast, examined the effects of MW while executing a filler task on a primary task that requires some degree of creativity. 1 Moreover, the filler task of the incubation period provides an optimal moment for MW to occur without having negative effects, since the performance in that task itself is not important. On the other hand, MW during the incubation period could have positive effects on creative performance that seem to outlast the break. However, this is in contrast to the performance in most other fields that demands one’s sustained attention (e.g., driving a car, reading a text for an exam, following a conversation) that could be distracted and therefore be interfered by MW over the whole time. Eventually, state-level MW might not be as detrimental in creative domains that include an incubation period as it is for other domains. Distinguishing between MW at the state level and at the trait level in future research could help to find some evidence for this hypothesis.

In terms of trait-level MW, Preiss et al. (2016) showed that students’ trait-level MW was positively associated with two scores of creativity suggesting that the more MW the participants experienced in their daily lives, the more creative they were. This finding is in-line with earlier daydreaming research that showed positive associations between measures of daydreaming and creative problem solving (e.g., Singer, 1966 ; Huba et al., 1977 ). Interestingly, the results of a recent study by Soemer et al. (2019) suggests that trait-level MW might actually have opposite effects on a given primary task. Replicating previous studies on MW during reading, they found a negative association between state-level MW and comprehension, whereas trait-level MW had two opposing effects on comprehension. First, there was a negative effect mediated by state-level MW meaning that trait-level MW was positively associated with state-level MW which in turn had a negative effect on comprehension. Second, there was a direct positive effect of trait-level MW on comprehension. Soemer et al. (2019) hypothesized that trait-level MW, like daydreaming, is composed of different dimensions (i.e., positive-constructive, poor attention etc.). Accordingly, the direct positive effect of trait-level MW might be related to elaborative processes occurring during reading; that is, individuals scoring high on their trait-level scale of MW presumably engaged in more elaborative processes during reading which, in turn, improved comprehension. This would be in accordance to findings of the daydreaming research that showed the positive-constructive type of daydreaming to be associated with the exploration of ideas and openness to new experiences (e.g., to allow for new unfamiliar thoughts; Tang and Singer, 1997 ). Unfortunately, to our best knowledge, no study has yet investigated the relationships between trait and state MW with creativity simultaneously.

Finally, it should be noted that each of the studies that investigated the association between MW and creativity was based on the hypothesis of an existing association between those constructs. However, a general caveat interpreting studies that fail to find evidence for a relation between MW and creativity is that non-significant hypothesis tests, as important as they may be, do not support the null hypothesis of no relation between MW and creativity. This is because the general framework of null hypothesis significance testing (NHST) does not allow for accepting the null hypothesis upon a non-significant result (see Nickerson, 2000 , for a thorough discussion).

Educational Implications

Overall, the reviewed body of research suggests that creativity is positively related to, at least, certain forms of MW. Creativity in turn, is known to promote various forms of learning (e.g., Hattie, 2009 ; Karwowski et al., 2020 ). We thus argue that educational practitioners should not blindly aim at reducing MW during a session but they should pay attention to the conditions that promote “beneficial” MW. In the following, we will make a number of suggestions on how to accomplish this.

One particular outcome of the reviewed studies is that breaks can help finding solutions to tasks requiring divergent thinking or gaining insight into a problem (i.e., creative problem solving). This outcome may not sound entirely new. Generations of teachers and learners have intuitively known that making a break and refresh one’s mind can lead to the solution of a problem ( Wallas, 1926 ). On the scientific side, early experimental research by the Russian psychologist Zeigarnik demonstrated that individuals who take a break from a given task and engage in task-unrelated activities (such as playing) will remember better what they did before the break than individuals that complete their task before the break ( Zeigarnik, 1927 ). More recent research in this field suggests that breaks may serve as incubation periods for creative problem solving and, therefore, should be introduced into classroom sessions ( Rae, 1997 ; Webster et al., 2006 ). In terms of the four-stage model of creative process ( Wallas, 1926 ), breaks provide space for the second stage, incubation, so the absence of a break during a creative task would be tantamount to skipping this important second phase of the creative process. Moreover, most learning tasks in school are treated “uninterruptable,” such as reading a long text to its end in order to earn the break first. In contrast, it might be useful for teachers to look for a suitable place for a short break within the learning material that allows learners for creative incubation and process what they have learned so far.

Going beyond the previous literature, however, a main contribution of the studies reviewed here is that they reveal MW as a potential mediator process for the effect of an incubation period for creative problem solving. Furthermore, some studies suggest that the activity carried out during the incubation period is an important factor to consider. In particular, this activity should be easy enough to allow for sufficient levels of MW ( Baird et al., 2012 ). Similarly, performing in no activity during the incubation period does not contribute to MW. A task too difficult, however, could not only hinder the creative idea generation during incubation stage of creative process but also shift the proportion of beneficial intentional MW to a more detrimental form, spontaneous MW ( Seli et al., 2016b ). Taken together, these findings highlight the importance of choosing an activity (in contrast to having no activity) with an easy level of difficult, to perform during a break. Fortunately, it has been found that easy to realize stimulus-response tasks can improve MW occurrence during incubation periods (e.g., Baird et al., 2012 ). On the other hand, tasks such as the SART are also capable of stimulating MW ( Tan et al., 2015 ), but they are limited to the laboratory and are hardly applicable in educational settings. It seems not too difficult to find other tasks that meet both requirements, meaning that they are beneficial to MW as well as easy to implement into breaks. However, unless there are any new findings, the scope of application is primarily limited to creative performance in divergent thinking tasks and insight problem solving. It remains to be evaluated whether these results can also be transferred to real teaching situations, as an earlier examination showed no evidence for a relation between performance in solving insight problems and real-world creative achievement as well as creative behavior ( Beaty et al., 2014a ).

General Conclusion

MW is often considered as an obstacle to performances in various domains of learning and cognitive functioning in general. However, many researchers have pointed out that MW occurs too often in daily life to simply represent a mere dysfunction of our brain (e.g., Mooneyham and Schooler, 2013 ; Schooler et al., 2013 ; Smallwood and Andrews-Hanna, 2013 ). Indeed, like these researchers, we argue that MW may actually serve an important cognitive function in our lives. One of these functions is to facilitate creative output in form of divergent thinking and creative problem solving, as suggested by several reviewed studies on the relation between MW and creativity. We further argue that because creativity is an important predictor of learning in various contexts, specific forms of MW occurring at the right time may actually promote certain learning tasks, in particular, when these tasks require original and appropriate solutions (i.e., creative problem solving).

That being said, evidence to support our claim is somewhat indirect and limited to the incubation paradigm and two subdomains of creativity (i.e., divergent thinking and creative problem solving). We therefore suggest that results from studies using the incubation paradigm should be transferred to more realistic learning contexts. In addition, future research addressing the relationship between MW and creativity should pay more attention to the different forms of MW.

Author Contributions

All authors listed have made a substantial, direct, and intellectual contribution to the work, and approved it for publication.

This work was funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) - Projektnummer 491466077.

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s Note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

• ^ Interestingly, a MW episode that occurs during the filler task may be classified as on-task behavior with regard to the primary task in this paradigm, if the episode deals with topics of the primary task.

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PubMed Abstract | Google Scholar

Keywords : mind wandering, creativity, divergent thinking, incubation effect, school learning, creative problem solving

Citation: Gericke C, Soemer A and Schiefele U (2022) Benefits of Mind Wandering for Learning in School Through Its Positive Effects on Creativity. Front. Educ. 7:774731. doi: 10.3389/feduc.2022.774731

Received: 12 September 2021; Accepted: 23 March 2022; Published: 15 April 2022.

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Copyright © 2022 Gericke, Soemer and Schiefele. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Christian Gericke, [email protected]

† These authors have contributed equally to this work and share senior authorship

Listen up, kids! How mind wandering affects immediate and delayed memory in children

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• Published: 27 December 2023

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• Jessica Cherry   ORCID: orcid.org/0000-0003-0634-4966 1 ,
• Teresa McCormack 1 &
• Agnieszka J. Graham 1  

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Mind wandering occurs when attention becomes disengaged from the here-and-now and directed toward internally generated thoughts; this is often associated with poorer performance on educationally significant tasks. In this study, 8- to 9-year-old children ( N = 60) listened to audio stories embedded with intermittent thought probes that were used to determine if participants’ thoughts were on or off task. The key objective was to explore the impact of probe-caught mind wandering on both immediate and delayed memory retention. Children reported being off task approximately 24% of the time. Most inattention episodes were classified as task-unrelated thoughts (i.e., ‘pure’ instances of mind wandering, 9%) or attentional failures due to distractions (9%). Higher frequency of mind wandering was strongly associated with poorer memory recall, and task-unrelated thoughts strongly predicted how well children could recall components of the audio story both immediately after the task and after a 1-week delay. This study is the first to demonstrate the impact of mind wandering on delayed memory retention in children. Results suggest that exploring mind wandering in the foundational years of schooling could provide the necessary empirical foundation for the development of practical interventions geared toward detecting and refocusing lapses of attention in educational contexts.

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People’s conscious experiences are not always tied to ongoing events and immediate surroundings. For example, while reading this article, you may suddenly realize that you are no longer paying attention to the text and that, in spite of your best efforts to maintain focus, you start thinking about what you are going to eat this evening. This shift of attention from the task at hand towards internally generated thoughts is often referred to as mind wandering (Murray et al., 2020 ; Smallwood & Schooler, 2006 ). Although specific definitions vary, in psychological research, mind wandering is typically operationalized as task-unrelated thought (Murray & Krasich, 2022 ). Studies of daily-life mind wandering indicate that it consumes a substantial amount of time (Seli et al., 2018 ) and can be costly in educational contexts because learning depends on extracting information from the learning environment and aligning this new information with existing knowledge (e.g., Sanchez & Naylor, 2018 ); mind wandering signals a breakdown in this process. To date, the link between mind wandering and learning has been studied primarily in adult student populations, generating a body of evidence in authentic settings of potential value to educators interested in its impact on educational outcomes (Szpunar, 2017 ).

Strikingly, despite its obvious educational significance, only a relatively small number of studies have attempted to examine mind wandering in children (Cherry et al., 2022 ; Frick et al., 2020 ; Jones, 2019 ; Keulers & Jonkman, 2019 ; McCormack et al., 2019 ; Van den Driessche et al., 2017 ; Wilson et al., 2022 ; Ye et al., 2014 ; Zhang et al., 2015 ). The findings of these studies are promising, though, in that they suggest that it is possible to take meaningful measures of inattentive episodes in children as young as around 7 years. Nevertheless, to date, little is known about the impact of mind wandering on children’s learning. Although it may seem probable, based on findings with adults, that mind wandering is detrimental to children’s learning, it is important to establish this empirically, in particular because it cannot be taken as a given that children’s reports of mind wandering are sufficiently robust as to be predictive of educationally significant outcomes.

Much work in the field of mind wandering has focused on understanding and quantifying the costs of mind wandering in learning environments, with most of the educationally relevant research in adults and adolescents suggesting that higher rates of mind wandering are associated with poorer comprehension and less learning. In studies of mind wandering while reading, participants read texts and periodically classify the focus of their thoughts as ‘on task’ or ‘off task’ (e.g., asked to judge whether they are thinking about The text ; How well I’m understanding the story ; A memory from the past ; Something in the future ; Current state of being ; Other ; McVay & Kane, 2012 ; Unsworth & McMillan, 2013 ) in response to structured probes, with certain off-task responses (e.g., A memory from the past ; Something in the future ) treated as a measure of mind wandering. These types of studies reliably show that participants who report more task-unrelated thoughts in response to probes during reading also tend to recall less of what they read than those who report fewer task-unrelated thoughts (McVay & Kane, 2012 ; Sanchez & Naylor, 2018 ; Smallwood et al., 2008 ; Unsworth & McMillan, 2013 ). Furthermore, mind-wandering rates are relatively stable in that those who mind wander more during one reading task also tend to mind wander more in others (Al-Balushi & Al-Harthy, 2015 ; McVay & Kane, 2012 ).

A complementary strand of evidence for the link between mind wandering and learning comes from studies embedding intermittent thought-probes within live or video-recorded lectures and other types of learning activities (e.g., discussions, problem-solving tasks, student presentations; Bunce et al., 2010 ; Cameron & Giuntoli, 1972 ; Locke & Jensen, 1974 ; Risko et al., 2012 ; Schoen, 1970 ; Shukor, 2005 ; Szpunar et al., 2013 ). Despite considerable variation in lecture durations, playback speed, and topics, adult participants consistently report mind wandering approximately 30%–40% of the time and, in line with the reading comprehension findings noted earlier, students who mind wander more tend to recall less of the lecture content (Hollis & Was, 2016 ; Kane et al., 2017 ; Murphy et al., 2023 ; Wammes et al., 2016 ; Wammes & Smilek, 2017 ). Higher rates of mind wandering have also been associated with taking fewer and poorer quality notes during lectures (Dewey, 2020 ; Jing et al., 2016 ; Kane et al., 2017 ; Szpunar et al., 2013 ; Wong & Lim, 2021 ) and with lower topic interest and motivation (Lindquist & McLean, 2011 ; Seli et al., 2015 ; Wammes et al., 2016 ). Furthermore, many studies with young adults have indicated that the occurrence of mind wandering increases for young adults as a function of time-on-task in lectures (Farley et al., 2013 ; Risko et al., 2012 ; Varao-Sousa & Kingstone, 2019 ). Interestingly, a growing body of literature suggests that older adults tend to mind wander less than younger adults when completing learning activities (14% vs. 40%, Jackson & Balota, 2012 ; 19% vs. 42%; Murphy et al., 2023 ). This trend also extends to other daily activities (Jordão et al., 2019 ; Maillet et al., 2018 ; Seli et al., 2017 ).

Although a handful of recent studies have attempted to look at mind wandering in the childhood period before adolescence (Frick et al., 2020 ; Jones, 2019 ; Keulers & Jonkman, 2019 ; McCormack et al., 2019 ; Van den Driessche et al., 2017 ; Wilson et al., 2022 ; Ye et al., 2014 ; Zhang et al., 2015 ) only Cherry et al. ( 2022 ) specifically investigated the link between off-task thinking and learning in children. In their study, 6- to 11-year-olds listened to an experimenter read out a story. Intermittently during the listening exercise, children were probed by a computer to report whether their thoughts were focused on the story they were listening to or whether they were thinking about something else. Immediately after the story ended, children completed a multiple-choice memory test based on the story material and indicated their situational interest in the story topic. Cherry et al. ( 2022 ) reported three key findings. First, children who reported more off-task episodes also showed poorer immediate recall for the information that they had just been exposed to. Second, children self-reported being off task around 25% of the time, a figure that did not change significantly with age, suggesting there may not be age differences in levels of off-task thoughts in school-aged children. The third finding was that situational interest had a significant indirect effect on memory recall via off-task thoughts.

While Cherry et al.’s ( 2022 ) findings indicate that off-task thoughts can be detrimental for children’s ability to recall information after a short delay, there is much that is still not known about the link between children’s mind wandering and their learning. Here, our aim was to begin to fill these gaps in existing knowledge, focusing on three specific issues. First, we sought to provide a more detailed analysis of children’s inattentive episodes by distinguishing between task-unrelated thoughts, task-related interference, and attentional lapses rooted in distractions. Our second objective was to assess the stability of mind wandering reports across two similar testing sessions. The third and final aim of the present study was to explore the impact of probe-caught mind wandering on delayed, rather than just immediate, memory recall. We now go on to describe the motivation behind each of these objectives.

Current study aims

Although Cherry et al. ( 2022 ) found a relation between off-task thoughts and children’s immediate memory recall, their measure of mind wandering did not distinguish between internally generated task-unrelated thoughts (i.e., ‘pure’ instances of mind wandering) and other forms of inattention. Mind wandering is typically characterized as involving a shift away from processing events in the external environment and towards self-generated thoughts; this perceptual decoupling makes mind wandering conceptually distinct from other forms of inattention, such as task-related interference or external distractions (Barron et al., 2011 ; Smallwood, 2013 ). Thus, thought-probe procedures that only distinguish between on-task and off-task thoughts might not provide a true estimate of ‘pure’ mind wandering, which may lead to erroneous inferences. Indeed, in studies with adults, participants not only routinely report mind wandering during lectures and while studying, they also frequently report being distracted by information in the external environment. A diary study where college students were asked to report on their attentional failures over the course of a week found that 31% of the reported attentional failures were due to distractions either while studying (22%) or while in class (9%; Unsworth, Brewer et al. 2012a , Unsworth, McMillan et al. 2012b ). Like mind wandering, external distraction reflects a general lapse of attention and accordingly can also impede performance, learning, and memory (Stawarczyk et al., 2011 ; Varao-Sousa et al., 2018 ). For example, Shelton et al. ( 2009 ) found that hearing a ringing mobile phone during a lecture resulted in lower retention for material presented at the same time as the ringing mobile phone, compared with material presented before the onset of the ringtone. In such situations attention is shifted from the current task to irrelevant (and potentially irritating) information in the external environment. External distractors can take on multiple forms including extraneous noises and sights (e.g., talking, other students moving around), or bodily sensations (e.g., feeling too hot or too cold, feeling hungry).

To provide a more accurate characterization of children’s inattentive episodes during learning activities, in the current study, child participants were asked to make a distinction between task-unrelated thoughts, task-related interference, and instances of inattention rooted in distraction during educational-style stories about historical events. In line with the extant literature in adults, task-unrelated thoughts were operationalized as thoughts that are unrelated to the ongoing, externally oriented, task (e.g., reminiscing about past events, contemplating the future, fantasizing). By contrast, task-related interference was defined as episodes involving evaluative thoughts about the task or about task performance (e.g., I’m not very good at this, I don’t find this interesting ; e.g., Sarason et al., 1986 ; Smallwood et al., 2004 ). In the area of mind wandering, reports of task-related interference are typically excluded from data analysis because, as noted by McVay and Kane ( 2009 ), task-related thoughts are an “ambiguous intermediary between on- and off-task thought” (p. 200) and thus subject to ambiguous interpretations (McVay & Kane, 2009 , 2012 ; Unsworth & McMillan, 2013 ).

To the best of our knowledge, Van den Driessche et al.’s ( 2017 ) study of children with and without attention-deficit/hyperactivity disorder (ADHD) is the first to date that has attempted to get children to subcategorize their off-task thoughts into task-unrelated thoughts, task-related interference, and instances of inattention rooted in distraction while completing a series of go/no-go trials. Their data suggest that children may be able to use these categories, although the age of their sample of children was wide (20 typically developing 6–12-year-olds). Building on their promising initial findings, in the present study we also asked children to further categorize their off-task thoughts, and then examined whether the link between mind wandering and learning reported by Cherry et al. ( 2022 ) remains intact when the index of mind wandering reflects only the frequency of task-unrelated thoughts. We also examined whether task-related interference and distraction were predictive of learning. Distinguishing between different categories of inattention is important because it is essential for informing how best to develop child-friendly strategies to enhance task-focused behavior during learning activities. Thus, our study examined whether children, like adults, can make such distinctions, and whether episodes in these different categories were differentially predictive of learning.

The second aim of the current study was to assess the stability of mind wandering reports. As already noted, adults’ mind wandering reports are relatively consistent in the sense that those who mind wander more during a particular task also tend to mind wander more in other similar tasks (Al-Balushi & Al-Harthy, 2015 ; McVay & Kane, 2012 ; Varao-Sousa & Kingstone, 2019 ). Although it is promising that the handful of studies that have used the probe-caught method with children provide comparable estimates of the frequency of off-task thoughts (25%, Cherry et al., 2022 ; 20-25%, Keulers & Jonkman, 2019 ; 33%, Zhang et al., 2015 ), to the best of our knowledge only Keulers and Jonkman’s ( 2019 ) study looked at the relation between the frequency of children’s off-task thoughts reported in one session and that reported in another. These authors found that, in 9- to 11-year-olds, levels of self-reported off-task thoughts were moderately correlated across the two sessions, despite the fact that the tasks completed in each session were quite different (i.e., a listening task versus a battery of executive function tasks). We sought to replicate this finding when distinguishing between different categories of off-task thought rather than just general inattentiveness, and also to examine whether the correlation may be stronger if the primary task was similar for the two testing sessions (listening to a story).

Our third key objective was to replicate and extend the results of Cherry et al. ( 2022 ) by measuring the impact of probe-caught mind wandering on not only immediate memory recall, but also delayed memory recall to test whether the effect is still present after a 1-week delay. In the adult literature, the longer-term impact of mind wandering on academic performance has been most often studied in the context of exam performance or end of term/year course marks (Kane et al., 2021 ; Mrazek et al., 2012 ; Wammes et al., 2016 ). The handful of studies that have included a delayed memory measure typically sought to demonstrate the effectiveness of different strategies at reducing mind wandering and, ultimately, improving academic success (e.g., Fenesi et al., 2018 ; Mills et al., 2021 ; Peterson & Wissman, 2020 ). Taken together, these studies clearly suggest that mind wandering is a useful predictor of delayed learning performance. However, it is still unknown whether self-reports of mind wandering in child populations is similarly predictive of long-term learning. Nevertheless, based on the adult findings, we anticipated that our mind wandering measure would predict delayed as well as immediate memory performance. Moreover, we also sought to examine the relation between level of interest in the listening task, mind wandering, and both immediate and delayed memory performance. Cherry et al. found that task interest had a significant indirect effect on memory performance via off-task thoughts, suggesting that participants with lower interest in the story topic were potentially more likely to engage in mind wandering with detrimental effects on their memory for the story contents. We hoped to replicate this finding in our study and extend it to include delayed as well as immediate recall.

The present study

To achieve the objectives listed above, each participant completed two testing sessions scheduled approximately one week apart. On both occasions, 8- to 9-year-old children listened to an audio story (one about a fictional Pharaoh based in ancient Egypt) at Time 1 [T1] and a different story (about a fictional species of dinosaurs based in the Cretaceous period) at Time 2 [T2] and reported if their thoughts were on or off task in response to child-friendly structured probes. If children reported being off task they were further instructed to categorize their thought as task-related (i.e., episodes of task-related interference), task-unrelated, or a result of distraction, with task-unrelated episodes conceptualized as instances of ‘pure’ mind wandering. Participants also completed two immediate memory tests (one after each story) and one delayed memory test probing their ability to recall key components of the story they had listened to 7 days prior. We also measured participants’ verbal ability and collected ratings of prior and situational interest in story topics.

Several predictions were formulated for the present research. We expected that children would report being off task approximately 20%–33% of the time, in line with previously published reports (Cherry et al., 2022 ; Keulers & Jonkman, 2019 ; Zhang et al., 2015 ), and that there would be a correlation between reported levels of off-task thoughts across T1 and T2. However, our primary purpose was not simply to establish rates of mind wandering in children, which might vary by context, but to examine the relative frequency of different types of episodes of inattention. Based on previous studies with adult participants conducted in educational environments, we expected task-unrelated thoughts and attentional lapses due to distraction to be more frequent than task-related interference (Kane et al., 2017 , 2021 ; Was et al., 2019 ). We anticipated that, using our measure of ‘pure’ mind wandering, we would find that higher levels of mind wandering during a learning activity are predictive of poorer immediate memory recall, consistent with the findings of Cherry et al. ( 2022 ). Furthermore, based on research with adult populations (e.g., Fenesi et al., 2018 ; Wammes et al., 2016 ) we hypothesized that the impact of mind wandering on memory recall would still be present after a weeklong delay. We also predicted that mind wandering would mediate the relationship between memory retention and ratings of interest in the topic of the story, replicating Cherry et al. ( 2022 ) among others (Hollis & Was, 2016 ; Soemer et al., 2019 ; Unsworth & McMillan, 2013 ).

Participants

The total sample included 61 8- to 9-years-olds (50.82% female, M age = 8.99 years, SD age = 0.52). A power analysis conducted using the ‘pwr’ package in R (Champely, 2012 ) indicated that this sample size was sufficient to detect medium linear regression effects at 80% power and α = .05. One participant was unable to attend the second testing session and their data were removed from the analyses. The final sample consisted of 60 children aged between 8–9 years (50% female, M age = 8.99 years, SD age = 0.52). Due to local demographics, the majority of participating children were white (98.33%) and of low to middle socioeconomic status.

All participants were recruited through parental interest generated by advertisements placed on social media platforms. Due to ongoing disruptions to face-to-face data collection caused by the COVID-19 pandemic, children were tested online using Microsoft Teams video-conferencing software. Over both testing sessions, most parents chose to stay in the same room as their children. Average scores obtained on the measure of verbal ability (Wechsler, 2014 ) indicated that participating children were just above the expected range ( M = 10.92, SD = 2.66, where 10 is the standardized average score).

Materials and procedure

Data collection took place over video-conferencing software via a series of PowerPoint presentations across two separate testing sessions approximately one week apart ( M = 6.93 days, SD = 1.91). The first testing session (T1) began with the researcher providing an age-appropriate overview of the study. Children then took part in an extensive training procedure; a cartoon character was introduced to explain the distinctions between different categories of thoughts (on-task, task-related interference, task-unrelated, and external distractions; see supplementary materials for additional details).

The children then engaged in a sorting activity to organize a sample of off-task thoughts into one of three boxes representing task-related interference (‘thoughts connected to the story’), task-unrelated thoughts (‘thoughts about other things happening at different times’), and attentional failures due to external distractions (‘thoughts about other things happening right now’). In the current study, 23.33% (14 children) made one or more errors in the initial sorting task, with the remaining 76.67% (46 children) successfully sorting the different off-task thoughts on their first try. Next, each child completed four practice trials. During practice, children listened to brief descriptions of the cartoon character’s thoughts before responding to thought probes. To answer each of the four probes, participants first made judgments on whether the cartoon character’s thoughts were on task (‘thinking about what was just said in the story’) or off task (‘thinking about something different’). If the cartoon character was thinking about things other than what was just said in the story, the children had to categorize the thought as task-unrelated, task-related interference or as an episode of inattention caused by external distraction. If the first four practice trials were completed successfully, children advanced to the listening activity. Otherwise, an additional four practice trials would commence. In the first set of training trials, the majority of participants (93.33%, n = 56) were able to correctly identify all four of the fictional character’s thoughts as on or off task, and when off task, they were accurately able to categorize the thoughts as task-unrelated thoughts, task-related interference or thoughts rooted in distraction. The remaining 6.67% (four children) made an error when sorting the fictional character’s thoughts in the first phase of practice questions but then went on to correctly answer the second set of practice questions. Error-free completion of at least one set of training trials was a prerequisite for taking part in the study. The precise wording of all task procedures can be found in the supplementary materials .

It was then explained to the children that they were going to listen to a story and that during that story they themselves would be probed about their thoughts to which they would answer verbally. At this point, children were asked to rate their general interest in the topic area (i.e., ancient Egypt at T1 and dinosaurs at T2) using a 5-point scale, ranging from I really don’t like it to I really like it . Another 5-point scale ranging from I really didn’t like it to I really liked it was displayed at the end of each story to gauge situational interest for the content of the listening task.

Once a rating of prior interest in the story topic was obtained, the story was played, and children were probed about their thoughts. The story about ancient Egypt was 1,854 words in length and lasted just over 12 minutes, the spoken word rate of about 2.5 words per second is regarded as the average speech production rate (Tauroza & Allison, 1990 ). To gain reliable and valid information about individual differences in mind wandering rate, and in line with recommendations from Welhaf et al. ( 2022 ), each story was embedded with eight intermittent thought probes that appeared on the screen approximately every 85 s (with a range of 65–105 s). Each probe consisted of an initial question dichotomizing whether the participant’s thoughts were on or off task (i.e., What were you thinking about just now? What was just said in the story or something different? ). If the participant was thinking about something different, they were asked to categorize their thought as task-unrelated, task-related interference, or driven by distraction. This was achieved by asking children to ‘place’ their thoughts into one of three boxes (i.e., Can you tell me in which box does your thought belong? Is the thought about other things happening at different times, is the thought about other things happening right now, or is the thought connected to the story? ). When participants instead indicated they were thinking about what was just said in the story, they were asked to answer a simple factual question by selecting one of two alternatives (e.g., How many sides does a triangle have? Three or four? ). Inclusion of this factual question ensured that number of questions asked after each probe was equal for all participants regardless of levels of on- or off-task thoughts; this follow-up question served as an attempt to make task completion time comparable across the entire sample. For a visual representation of the question layout see Fig. 1 ; note that all text displayed on the screen was also audio-presented to all children.

The structure of each thought probe. Participants were asked to answer verbally (i.e., I was thinking about what was just said in the story or I was thinking about something different ). The sequence on the left side was presented if the child reported on-task thoughts, whereas the right side shows the pathway taken if thoughts were reported as off task. Topic interest was measured both before and after each story. (Color figure online)

When the mind wandering task ended, participants indicated their situational interest in the story they just listened to (see Fig. 1 ) and completed an immediate memory retention test consisting of 10 questions in a multiple-choice format with three alternatives. All questions were derived from novel material presented within the fictional stories to ensure answers could not be based on participants’ prior knowledge on the topics. As there were two sets of 10 item memory test questions about ancient Egypt (one presented immediately after the story and another presented after a one-week delay), the tests were administered in a counterbalanced order (Set A/B at T1, followed by Set B/A at T2). All questions were scored as correct (1) or incorrect (0).

Finally, at the end of the first session, all children had their verbal ability assessed using the vocabulary subtest from the WISC-V (Wechsler, 2014 ). The vocabulary subtest required children to either name or define a range of items with the prompt “What is this?” or “What does . . . mean?” The first four items were picture items, the following items were all presented orally. The children could achieve a score of 0, 1, or 2 depending on the accuracy of their response.

The second testing session (T2) followed a similar format. After a brief overview of the session, the children completed another thought sorting activity and four or eight training trials. Similar to T1, the majority of participants completed the first four training trials without error (93.33%, n = 56). Following the successful completion of training procedures, children were asked to rate their interest in dinosaurs before listening to the second audio story. The listening activity about dinosaurs had a word length of 1,517 words and lasted just over 10 minutes, again the speech rate of 2.5 words per second is regarded as normal speech rate. As was the case at T1, the audio story contained 8 intermittent thought probes. When the story had finished, the children rated how much they enjoyed it and also completed two sets of multiple-choice questions. The first set of questions tested children's ability to recall content from the story they had just listened to (i.e., the story about dinosaurs). The following 10 questions tested children's delayed memory of the information from the story played at T1 (i.e., the story about ancient Egypt). If the children were shown Set A questions about the story at T1, they completed Set B at T2; if Set B was completed at T1 they received Set A at T2. The structure of the testing sessions is outlined in Fig. 2 .

The structure of the testing sessions. At T1 participants were played the story about ancient Egypt; immediately after listening to the story they answered 10 memory questions about the story. At the end of this testing session, participants had their vocabulary ability assessed. Approximately a week later at T2, children listened to a new audio story about dinosaurs. Immediately after the children completed a memory test about that story. Finally, at the end of T2, children answered 10 memory questions about the Egypt story they had listened to at T1 to test delayed memory. As there were two sets of 10 memory test questions about ancient Egypt (i.e., 20 items total) these memory tests were presented in a counterbalanced order. Children who completed Set A at T1 went on to complete Set B at T2 and the other set of children who completed Set B at T1 then went on to complete Set A at T2

The study was approved and conducted in accordance with the guidelines of the Faculty Ethics Committee of the authors’ university. Parents were fully debriefed and provided informed consent for participation prior to testing. Children also provided assent prior to and on both days of testing. All participants received a voucher worth £10 (British pounds) for completing both testing sessions.

Data were analyzed using R (R Core Team, 2021 ). A summary of task performance and normality values is provided in Table 1 . Across both testing sessions children reported being off task 24.17% of the time. Summed across both sessions, task-unrelated thoughts were reported most often—9.48% of the time—while the frequency of task-related interference was 5.52%. Attentional failures rooted in external distraction accounted for 9.17% of probe-caught responses. To assess if attentional states differed significantly between the first half and the second half of the listening activities, a series of paired t tests were run using the ‘t,test’ function in R (visual depiction of off-task probe responses can be found in Fig. 3 ). At T1, there was a significant effect of time on task, t (59) = −2.27, p = .027 for task related interference only. Participants reported more task -related interference in the second half of the activity (8%) compared with the first half of the activity (3%). None of the other attentional states varied significantly during the first and second half of the listening activity ( p > .05). At T2, participants reported being on-task significantly more as the story progressed from the first half (73%) to the second half (80%), t (59) = −2.12, p =  .039. This was also marked by fewer thoughts about distractions in the second half (8%) compared with the first half of the activity (13%), t (59) = −2.26, p = .028. Rates of mind wandering and task-related interference did not differ significantly from the first half to the second half of the listening activity at T2 ( p > .05).

Line graphs depicting the frequency of different types of off-task thoughts across the duration of both testing sessions. (Color figure online)

Overall, the index of mind wandering had good split-half reliability between T1 and T2 (Spearman–Brown coefficient = 0.88). When probe responses are split into groups (i.e., two groups of four probes from T1 and another two groups of probes from T2), the split-half reliability remains strong on all comparisons (Spearman–Brown coefficients = 0.59 to 0.75). We also found that rates of total numbers off-task thoughts were highly positively correlated, r (58) = 0.85, p < .001, across the two testing sessions: Children who reported more off-task thoughts when listening to the story about ancient Egypt were also more likely to report off-task thoughts when listening to the story about dinosaurs one week later. A correlation matrix was computed for the three categories of off-task thought (task-unrelated thought, task-related interference, and thoughts rooted in distraction) reported at T1 and T2 (Table 2 ). We found a moderately large significant correlation between task-unrelated thoughts reported one week apart, r (58) = 0.67, p < .001, suggesting that children who mind wandered more during the audio story played at T1 were also more likely to report task-unrelated thoughts during the audio story at T2. A similar but weaker link was observed for task-related interference, r (58) = 0.34, p = .007, indicating that evaluative thoughts about the task and about task performance may be relatively stable across similar learning activities. Finally, a moderately large significant correlation was found between the proportions of time children reported being distracted at T1 and T2, r (58) = 0.66, p < .001. For each type of off-task thought, the largest cross-session correlations were consistently within each subcategory.

With regards to memory recall, the two sets of 10 items testing knowledge of the ancient Egypt story had a Spearman–Brown coefficient of 0.48, and the 10 items on the dinosaur story test had a Spearman–Brown coefficient of 0.49.

Relations with memory performance

A partial correlation matrix was constructed to explore the associations between the different types of on- and off-task thoughts and memory performance (Table 3 ), controlling for age (in months), topic interest, and raw vocabulary score. The overall proportion of off-task thoughts was significantly negatively correlated with both immediate and delayed recall. When off-task thoughts were further categorized, only mind wandering (i.e., the frequency of thoughts categorized as task-unrelated) was consistently negatively associated with both immediate and delayed memory performance. The rate at which children reported experiencing distractions was negatively associated with immediate memory recall only at T2 and frequency of task-related interference only at T1.

The links between task-unrelated thoughts and immediate memory recall are depicted in the paired-point graph in Fig. 4 , and the link between task-unrelated thoughts and delayed memory retention is displayed in Fig. 5 (note that the graphs depict the proportional values of task-unrelated thoughts and memory test performance rather than raw scores).

Paired-points graph demonstrating the links between task-unrelated thoughts (proportional score out of 8 probes) and immediate memory recall (proportional score out of 10 questions). Black lines indicate median; the lower and upper hinges correspond to the first and third quartiles; whiskers depict maximum and minimum values within 1.5 times the interquartile range

Paired-points graph illustrating the link between task-unrelated thoughts reported at T1 (proportional score out of 8) and delayed memory recall measured at T2 (proportional score out of 10). Black lines indicate median; the lower and upper hinges correspond to the first and third quartiles; whiskers depict maximum and minimum values within 1.5 times the interquartile range

Prior to regression analyses, proportional data were arcsine transformed to stabilize variance and meet the necessary assumptions required for linear models. To investigate the impact of off-task thoughts on immediate memory performance we pooled the relevant data obtained at T1 and T2 before conducting multiple linear regression analyses (Table 4 ). To account for each participant having two data points (i.e., performance at T1 and performance at T2), participant ID was added as a random intercept in this analysis which was performed using the ‘lm’ function, alongside the ‘lm.beta’ package, in R (Behrendt, 2022 ). First, age and raw vocabulary score were added to the null model which accounted for a moderate amount of variance in immediate memory performance ( R 2 adjusted  = .10,  F [2, 117]= 7.37,  p = .001). At this step, only vocabulary ability was identified as a significant predictor (β = 0.34, p < .001). Next, different forms of inattention—task-unrelated thoughts, distractions, and task-related interference—were added successively to the null model (see Models 1–3 in Table 4 ). In the final model (Model 3), task-related interference (β = −0.24, p = .003), thoughts due to distraction (β = −0.32, p < .001), task-unrelated thoughts (β = −0.29, p < .001), and vocabulary ability (β = 0.40, p < .001) were all identified as significant predictors of immediate memory recall. Model comparison using the ‘anova’ function in R, revealed that the final model provided the best fit for the data, F (4, 114) = 8.90, p = .003. The results indicate that all forms of inattention are detrimental to immediate memory performance.

Another key aim of this study was to explore the impact of off-task thoughts, particularly instances of ‘pure’ mind wandering, on delayed memory performance; to this end, another multiple linear regression analysis was conducted (Table 5 ). Age and raw vocabulary score were entered into the regression analysis to formulate the null model which did not significantly explain variance in delayed memory performance ( R 2 adjusted  = .05,  F [2, 57] = 2.66,  p = .078), although vocabulary ability was identified as a significant predictor (β = 0.30, p = .027). Similar to the analyses described in the previous paragraph, task-unrelated thoughts, distractions, and task-related interference were added to the null model one by one to assess the contribution of different forms of inattention to delayed memory performance (see Models 1–3 in Table 5 ). Overall, Model 2 (containing age, vocabulary score, and task-unrelated thoughts) was deemed to have the best fit, F (2, 56) = 5.90, p = .018. Neither thoughts due to distraction nor task-related interference emerged as significant predictors of delayed memory recall.

Relations with topic interest

Topic interest ratings obtained before and after the story (i.e., prior topic interest and situational topic interest, respectively) were moderately strongly positively correlated, r (118) = 0.52, p < .001. Next, a correlation matrix was computed to assess the relationship between topic interest, mind wandering, and memory performance (Table 6 ). For all measures bar delayed memory accuracy, data obtained at both T1 and T2 were pooled together prior to analysis. As shown in Table 6 , situational topic interest ratings (i.e., ratings gathered after children listened to the stories) appear to be more closely linked to indices of inattention and memory performance. Overall, however, the robust associations between topic interest, mind wandering, and memory accuracy observed in previous studies do not emerge consistently in the present experiment.

To investigate if mind wandering mediates the relationship between topic interest and memory performance a series of mediation analyses were computed with the ‘lavaan’ package in R (Rosseel, 2012 ) using bootstrapping with 5000 samples (reported in Table 7 ). First, to replicate the approach taken by Cherry et al. ( 2022 ), we tested if situational topic interest had an indirect effect on immediate memory performance via off-task thoughts. For this analysis, the outcome variable was immediate memory performance, the predictor variable was situational topic interest, and the mediator variable was off-task thoughts. To account for participants having two data points (performance from T1 and T2), participant ID was added as a random intercept when constructing the mediation model. Using this approach, and contradictory to Cherry et al., the effect of topic interest on immediate memory recall was not found to be mediated by off-task thoughts ( b = 0.18, SE = 0.10, 95% CI [0.01, 0.39], p = .051). The second mediation model was built with situational topic interest as the predictor, delayed memory performance as the outcome, and off-task thoughts at T1 as the mediation variable. Although the mediation revealed a significant direct effect of situational topic interest on delayed memory performance ( b = 0.50, SE = 0.23, 95% CI [0.01, 0.90], p = .026), off-task thoughts were not found to mediate this effect ( b = 0.10, SE = 0.08, 95% CI [−0.03, 0.26], p = .183).

Two further mediation analysis were conducted to investigate if the more specific index of mind wandering (i.e., the proportion of task-unrelated thoughts) mediates the relationship between memory recall and ratings of interest in the topic of the story (Table 7 ). In the third mediation model, immediate memory performance was entered as the outcome variable and situational topic interest was entered as the predictor variable; task-unrelated thoughts were entered as the mediator variable. For the final mediation analysis, ratings of situational topic interest were entered as the predictor, task-unrelated thoughts were entered as the mediator and delayed memory performance was entered as the outcome. Neither model revealed a significant indirect effect of topic interest on memory performance via mind wandering (for immediate memory: b = 0.13, SE = 0.08, 95% CI [−0.02, 0.29], p = .111; for delayed memory: b = 0.08, SE = 0.08, 95% CI [−0.04, 0.25], p = .287).

The present study applied a more nuanced approach to children’s off-task reports with the introduction of distinct off-task thought categories spanning task-unrelated thoughts, task-related interference, and inattention due to distraction. Using this approach, we aimed to examine the consistency of mind wandering reports across different testing sessions. A further aim was to examine whether mind wandering was predictive of both immediate and delayed memory performance, and whether mind wandering mediated the relationship between topic interest and memory recall. To summarize our key findings, we found that children reported engaging in mind wandering around 9% of the time, with more task-unrelated thoughts being reported during the listening activity at T1 (12%) compared with T2 (7%). Overall, children reported off-task thoughts around 24% of the time and this global estimate of inattention did not change significantly between T1 and T2; there were also strong correlations between levels of off-task thoughts across the two sessions. In the present study ‘pure’ mind wandering did not increase as a function of time-on-task, a finding frequently reported with young adults attending lectures (Farley et al., 2013 ; Risko et al., 2012 ; Varao-Sousa & Kingstone, 2019 ), although there was some limited and inconsistent evidence that other types of off-task thoughts increased over the testing period. Importantly, probe-caught ‘pure’ mind wandering, operationalized specifically as task-unrelated thoughts, significantly predicted how well 8- to 9-year-olds remembered key components of an audio story both immediately after listening to it and after a delay of 7 days. Contrary to our predictions, though, we did not find any significant indirect effect of topic interest on either immediate or delayed memory retention that was mediated by mind wandering. How each of these findings relate to the existing research literature will now be discussed.

Types of inattentive episodes

One of our key aims was to provide a more precise estimate of childhood mind wandering. Motivated by the extant literature on adult populations (e.g., Kane et al., 2017 , 2021 ; Stawarczyk et al., 2014 ; Unsworth & McMillan, 2014 ), we developed age-appropriate thought probes that would allow children to distinguish between task-unrelated thoughts, task-related interference, and thoughts rooted in distractions. Despite the fact that this involved children making a more complex judgment than simply whether their thoughts were on task or off task, the data revealed robust cross-session correlations within categories suggesting that children as young as 8 years of age were able to sort their off-task thoughts into the three aforementioned categories. Prior to this study, most estimates of probe-caught childhood mind wandering were obtained by asking children to simply distinguish if their thoughts were on-task or off task, with off-task thoughts being used as the measure of mind wandering (Cherry et al., 2022 ; Keulers & Jonkman, 2019 ; Ye et al., 2014 ; Zhang et al., 2015 ). The introduction of more specific thought probes suggests that ‘pure’ instances of mind wandering occurred just over 9% of the time, indicating that previous estimates of childhood mind wandering (20%–33%) may potentially have been inflated. That is, these previous estimates likely included other types of inattentive episodes rather than simply mind wandering. To the best of our knowledge, the only previous study with child participants that has looked in more detail at types of probe-caught inattentive episodes is that of Van den Driessche et al. ( 2017 ), who also distinguished between inattention due to distraction, task-related interference, and mind wandering (these authors included an additional category of mind blanking, the mind seeming empty, to test a hypothesis about the specific deficit in ADHD). They found a very similar level of ‘pure’ mind wandering in their sample of typically-developing control children (8.3%) aged 6–12 years, and moreover similar levels of inattention due to distraction as we did (8%–10%), despite the fact that they used a very different primary task (a multitrial go/no-go task) and testing context. Of note is that, when operationalized specifically as task-unrelated thoughts and distinguished from other types of inattentive episodes, levels of childhood ‘pure’ mind wandering appear to be below those reported by adults engaging in similar listening activities (20%–24%, Varao-Sousa et al., 2018 ) and in executive functioning tasks (21%, Stawarczyk et al., 2014 ; 13%–24%, Unsworth & McMillan, 2014 ).

There are a number of possible explanations for this apparent difference. It may be that there is no genuine developmental difference, but instead differences in reported levels of mind wandering between children in our study and adults in other studies reflect procedural differences, such as the nature of the primary task or the testing context in which it was set, and that if adults were tested in a similar way, we would find similar levels of mind wandering as in our child sample. One reason for thinking this is not the correct explanation is that Van den Driessche et al. ( 2017 ) tested both child and adult participants with almost identical procedures. Although they did not statistically compare these samples, inspection of their data (see Fig. 1 in Van den Driessche et al., 2017 ) suggests that adults were reporting substantially more instances of mind wandering than children (around double) but similar levels of inattentive episodes due to distraction. Thus, their data suggest that there may indeed be a developmental increase in levels of ‘pure’ mind wandering.

It was not the aim of our study was to examine developmental change, and thus we did not include different age groups, but we note that the idea that levels of ‘pure’ mind wandering increase developmentally is at least consistent with the claim that maintaining a continuous train of internally-generated thought is resource-dependent (Smallwood, 2013 ; Smallwood & Schooler, 2015 ) and thus may increase with cognitive development. At first sight, such a claim may seem at odds, for example, with existing findings suggesting that aspects of children’s executive function are either negatively associated with levels of off-task thoughts or show no relation to them (Keulers & Jonkman, 2019 ; Wilson et al., 2022 ). However, these previous developmental studies did not specifically isolate ‘pure’ mind wandering; moreover, as Smallwood and Schooler ( 2015 ) suggest, the relation between executive functioning and mind wandering may be complex and context specific. In particular, cognitive resources may play different roles with regard to preventing lapses in attention to a primary task (lapses may reflect a failure of cognitive control; McVay & Kane, 2010 ) and sustaining trains of internally generated thoughts (which may itself demand cognitive resources). The debate about the relation between cognitive resources and mind wandering remains ongoing (Wong et al., 2022 ), and our findings do not speak to this issue as we did not take any cognitive measures (other than levels of vocabulary). Nevertheless, when put alongside those of Van den Driessche et al. ( 2017 ), as well as findings suggesting levels of mind wandering decline at the other end of the lifespan (Jackson & Balota, 2012 ; Jordão et al., 2019 ; Maillet et al., 2018 ; Murphy et al., 2023 ; Seli et al., 2017 ), the results of the current study suggest it may be important to examine the developmental profile of ‘pure’ mind wandering in future studies. This would involve testing a broader age range of children as well as examining whether there are developmental changes beyond childhood.

In addition to examining mind wandering, we also included a category of inattentive episode that mapped to task-related interference. This was the most infrequently reported category in our study, and our data differ in this respect from those of Van den Driessche et al. ( 2017 ) who found it to be the most frequently reported type of inattentive episode in their child sample. These differences may be due to the different primary tasks used in the studies: children were listening to a story in our study, whereas in Van den Driessche et al.’s paradigm thought probes were inserted between trials in a go/no-go task; in the latter context it is plausible that participants were more likely to be monitoring how they were getting on in the task or might have been more likely to be evaluating what was an unfamiliar task. Such an interpretation would be compatible with findings suggesting levels of task-related interference are related to task difficulty (McVay et al., 2013 ; Zavagnin et al., 2014 ). As with ‘pure’ mind wandering, it would be useful to examine whether there is developmental change in levels of self-reported task-related interference, not least because, unlike mind wandering, levels of task-related interference seem to increase at the other end of the life span (Jordano & Touron, 2018 ; McVay et al., 2013 ).

Consistency across sessions

A further aim of the current study was to assess the stability of mind wandering reports. We measured mind wandering twice, across two testing sessions that were similarly structured but involved different story content. Under these circumstances we found substantial across-session correlations in the overall proportion of off-task thoughts ( r = 0.85). This link appears to be stronger than that reported by Keulers and Jonkman ( 2019 ; r = 0.41), speculatively because the primary task used in our study was very similar across the two sessions. Looking at the rates of ‘pure’ mind wandering specifically, the link between the frequency of task-unrelated thoughts reported across the two times points appears to be large ( r = 0.67), consistent with the adult findings from McVay and Kane ( 2012 ) who reported that mind wandering measures from four different tasks as diverse as the go/no-go task and reading War and Peace loaded onto a single latent variable. Al-Balushi and Al-Harthy ( 2015 ) reported a similar finding concluding that probe-caught mind wandering rates during reading exercises were stable for university students across two types of chemistry based textual narrations ( r = 0.42). With similar patterns emerging from the thought probes across both testing sessions in our study, these correlations suggest that the current procedure is a reliable way of measuring off-task thoughts in children, and indeed the measurements of all three subcategories of off-task thoughts showed this type of reliability. The cross-session correlations in mind wandering also suggest that the tendency to engage in mind wandering may potentially be a stable characteristic even in children, although establishing this would involve testing across a much wider range of task contexts.

We note that one difference between our method and that of other studies of children’s mind wandering was that we used a two-stage procedure in which children initially categorized their responses as on- or off-task and then were asked a further follow up question. The nature of that follow-up question varied depending on whether children had reported themselves to be on or off task: If children reported being on-task they were asked a filler question (about the number of sides on a simple shape), whereas if they were off-task they further subcategorized their off-task thought. Follow-up questions were asked in both instances to ensure the overall number of questions children were asked was constant, regardless of how often they were on or off task. However, we acknowledge that there may be other ways to probe children’s thoughts that avoid the need for follow-up questions, making filler questions unnecessary. For example, it might be possible to get children to give a single response to one more lengthy probe question (e.g., ‘ What were you thinking about just now? Select one of the following: ‘the story’, ‘other things happening right now’, ‘other things happening at other times’ or ‘things connected with the story’) . Research on children’s mind wandering is still relatively limited and future studies need to establish exactly how best to elicit reliable self-reports from children.

Mind wandering and learning

The preliminary link between mind wandering and learning found by Cherry et al. ( 2022 ) remained intact when using a more stringent index of mind wandering comprising only task-unrelated thoughts. As expected, high levels of mind wandering during the learning activities were predictive of poorer memory recall of the novel information presented within the audio stories. These results are in line with previous adult studies demonstrating a link between mind wandering and memory recall (e.g., Hollis & Was, 2016 ; Kane et al., 2017 ; McVay & Kane, 2012 ; Sanchez & Naylor, 2018 ; Smallwood et al., 2013 ; Wammes et al., 2016 ; Wammes & Smilek, 2017 ). In this study, we also found that all categories of off-task thoughts (task-unrelated thoughts, task-related interference and inattention due to distractions) were linked to poorer immediate memory performance. Regardless of their origin, off-task thoughts can impact instantaneous memory recall to varying degrees, indicating that, like mind wandering, other types of inattention can attenuate the learning process (Shelton et al., 2009 ; Varao-Sousa et al., 2018 ). However, further regression analyses suggested that task-related interference and inattention due to distractions were not significantly linked to delayed memory retention in the same way as task-unrelated thoughts.

This latter finding provides some support for the conceptual premise that mind wandering differs from other types of inattention (Barron et al., 2011 ; Smallwood et al., 2008 ). We found that the link between ‘pure’ mind wandering and memory was still present after a weeklong delay, consistent with the idea that mind wandering can particularly hinder the sort of deep semantic encoding of information that supports long term memory (Thomson et al., 2014 ). More broadly, if mind wandering affects children’s long-term retention of information, it may be linked to more global indices of academic achievement, in parallel to the way that probe-caught mind wandering aligns closely with longer term learning outcomes in adults, such as course grades (Kane et al., 2021 ; Mrazek et al., 2012 , Wammes et al., 2016 ). To develop a more detailed account of the educational significance of mind wandering in the foundational years of schooling, future studies need to examine whether, and under what circumstances, indices of mind wandering serve as predictors of children’s scores on tests measuring academic performance in core subject areas. Establishing this could prove useful in developing strategies to enhance children’s learning in educational contexts by reducing mind wandering.

Mind wandering and topic interest

At the outset of the study, we predicted that mind wandering would mediate the relationship between memory recall and topic interest, replicating previous work with adolescent and adult populations (Hollis & Was, 2016 ; Soemer et al., 2019 ; Unsworth & McMillan, 2013 ) and with children (Cherry et al., 2022 ). This finding did not emerge from the data. Topic interest did not have a significant indirect effect on memory recall via mind wandering or general off-task thoughts, contradicting several studies which found higher topic interest to be associated with less mind wandering in both youth and adult populations (e.g., Linquist & McLean, 2011 ; Seli et al., 2015 ; Soemer et al., 2019 ; Unsworth & McMillan, 2013 ). This inconsistency may have been driven by a procedural detail – namely, the fact that topic interest ratings were obtained twice. Children were first asked to rate their interest in the overall story topic prior to listening to it using a five-point Likert scale. Then after listening to the audio recording, children were asked to use the same scale to report their interest in the actual story they had just listened to. It is a possibility the scales were worded too similarly, and a strong body of research suggests repeated questioning can lower the veracity and consistency of children’s answers (Bonawitz et al., 2020 ; Fivush & Schwarzmueller, 1995 ). Alternatively, the association between topic interest, mind wandering, and memory performance is not as consistent and salient for younger children as it is for adults. Future work taking into account other factors that are often investigated in parallel with topic interest (e.g., task difficulty and motivation; Guthrie et al., 2007 ; Hidi & Harackiewicz, 2000 ; Soemer & Schiefele, 2019 ; Unsworth & McMillan, 2013 ) is needed to expand our understanding of how topic interest is likely to influence childhood mind wandering and learning.

We set out to improve our knowledge of mind wandering in children by focusing on three specific issues. First, we sought to provide a more precise measure of mind wandering in children and to this end, our findings suggested that 8- to 9-year-olds are appropriately able to distinguish between attentional lapses that are rooted in task-unrelated thoughts, task-related interference, and those that are a result of distraction. The second aim of the present study was to assess the stability of mind wandering reports in children; we found that probe-caught mind wandering remained stable across both testing sessions. Our final objective was to explore the impact of mind wandering on delayed memory recall. This study was the first to uncover task-unrelated thoughts, as opposed to general off-task thinking, as an important predictor for both immediate and delayed memory recall in children. Taken together, the current findings indicate greater investigation on children’s attentional focus during learning activities within the classroom could provide a springboard for the development of strategies geared towards equipping children with the necessary skills to detect and refocus lapses of attention to improve overall learning outcomes.

Data availability

Materials, data, and analysis code are available on the Open Science Framework at https://osf.io/76chp/?view_only=68fbcc54b138435ca3797f74244166c7 . The authors declare no conflict of interest.

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It’s normal for your mind to wander. Here’s how to maximise the benefits

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Have you ever found yourself thinking about loved ones during a boring meeting? Or going over the plot of a movie you recently watched during a drive to the supermarket?

This is the cognitive phenomenon known as “ mind wandering ”. Research suggests it can account for up to 50% of our waking cognition (our mental processes when awake) in both western and non-western societies .

So what can help make this time productive and beneficial?

Mind wandering is not daydreaming

Mind wandering is often used interchangeably with daydreaming. They are both considered types of inattention but are not the same thing.

Mind wandering is related to a primary task, such as reading a book, listening to a lecture, or attending a meeting. The mind withdraws from that task and focuses on internally generated, unrelated thoughts.

On the other hand, daydreaming does not involve a primary, active task. For example, daydreaming would be thinking about an ex-partner while travelling on a bus and gazing out the window. Or lying in bed and thinking about what it might be like to go on a holiday overseas.

If you were driving the bus or making the bed and your thoughts diverted from the primary task, this would be classed as mind wandering.

The benefits of mind wandering

Mind wandering is believed to play an important role in generating new ideas , conclusions or insights (also known as “aha! moments”). This is because it can give your mind a break and free it up to think more creatively.

This type of creativity does not always have to be related to creative pursuits (such as writing a song or making an artwork). It could include a new way to approach a university or school assignment or a project at work. Another benefit of mind wandering is relief from boredom, providing the opportunity to mentally retreat from a monotonous task.

For example, someone who does not enjoy washing dishes could think about their upcoming weekend plans while doing the chore. In this instance, mind wandering assists in “passing the time” during an uninteresting task.

Mind wandering also tends to be future-oriented. This can provide an opportunity to reflect upon and plan future goals, big or small. For example, what steps do I need to take to get a job after graduation? Or, what am I going to make for dinner tomorrow?

Read more: Alpha, beta, theta: what are brain states and brain waves? And can we control them?

What are the risks?

Mind wandering is not always beneficial, however. It can mean you miss out on crucial information. For example, there could be disruptions in learning if a student engages in mind wandering during a lesson that covers exam details. Or an important building block for learning.

Some tasks also require a lot of concentration in order to be safe. If you’re thinking about a recent argument with a partner while driving, you run the risk of having an accident.

That being said, it can be more difficult for some people to control their mind wandering. For example, mind wandering is more prevalent in people with ADHD.

Read more: How your brain decides what to think

What can you do to maximise the benefits?

There are several things you can do to maximise the benefits of mind wandering.

• be aware : awareness of mind wandering allows you to take note of and make use of any productive thoughts. Alternatively, if it is not a good time to mind wander it can help bring your attention back to the task at hand

context matters : try to keep mind wandering to non-demanding tasks rather than demanding tasks. Otherwise, mind wandering could be unproductive or unsafe. For example, try think about that big presentation during a car wash rather than when driving to and from the car wash

content matters : if possible, try to keep the content positive. Research has found , keeping your thoughts more positive, specific and concrete (and less about “you”), is associated with better wellbeing. For example, thinking about tasks to meet upcoming work deadlines could be more productive than ruminating about how you felt stressed or failed to meet past deadlines.

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How to tackle mind-wandering in the classroom  

The human brain is built to be miserly. We continually hoard mental energy so that we are always ready to fulfil our evolutionary fight-or-flight needs. 

To save this mental energy, we all engage with the act of mind-wandering which, as every teacher knows, can prove costly for pupils’ learning and remembering.

Recent research on mind-wandering, involving more than 90 pupils aged between six and 11, revealed that mind-wandering occurs about 20 to 30 per cent of the time when listening to a story. Unsurprisingly, this common act of mind-wandering has a negative impact on how much of the narrative pupils can remember. 

This research, by Jessica Cherry and colleagues at Queen’s University Belfast, supports previous research undertaken with adults, and suggests that mind-wandering appears to be an instinctive energy-saving strategy for our brains, young or old.

More by Alex Quigley:

• Sats: How can schools address the primary writing crisis?
• Why even flawed research matters in education
• Does reading fluency really matter?

Most teachers won’t be wholly surprised by these findings. We have all experienced that pupil staring aimlessly beyond our shoulder or peering out of the classroom window.

However, given that this seemingly instinctive strategy is so common, I’d argue it’s not worth much of our attention when planning to teach. Instead, simply knowing and understanding the persistent nature of mind-wandering can help us to consider how to address it so that it doesn’t compromise our pupils’ learning and remembering too significantly.

It is also worth considering the benefits that mind-wandering may bring. Some research on working memory suggests that mind-wandering isn’t a complete waste of time. A study published by Christine Godwin, and other US researchers in 2017 revealed, for example, that those who reported more frequent daydreaming scored higher on tests to measure intellectual creativity.

However, it’s clear that we can’t encourage children to mind-wander all the time; there are critical times in the classroom where there needs to be sustained attention on what is being taught. So how can we discourage unhelpful mind-wandering?

Research published by Evan Risko and colleagues, from the University of Memphis in 2013 and involving older students in university lecture halls, revealed that some learning tools may encourage more unhelpful mind-wandering than others. 

Even among these adult students, the distraction of mobile phones and laptops overloaded their brains. Those students who paid attention to emails in lectures performed worse in subsequent memory tests. 

This calls into question the use of laptops for note-making. Perhaps the learning benefit of using phones or laptops in the classroom may need to be revised if such activity supercharges mind-wandering?

It may be, though, that a few small tweaks to teaching can improve attention and increase pupils’ learning and remembering. One consistent finding from research is that mind-wandering can be reduced when people are interested in the topic at hand. Of course, we cannot make every topic in the classroom compelling but we can utilise creative hooks that instigate interest. For instance, you can begin a topic with a collage of interesting images and ask for connections to the new topic at hand.

Another everyday strategy to mitigate mind-wandering is targeted questioning. “Cold Calling” (a Doug Lemov Teach Like a Champion strategy) - that is to say, asking pupils questions by name and with conscious forethought of which children to engage - ensures that they have to allocate more attention to the potential of a question than a general “Can anyone tell me…?”

With more awareness of mind-wandering, we can rescue the attention of pupils and make a meaningful difference to their learning.   

The link between mind wandering and learning in children - ScienceDirect Everyday attention: Mind-wandering and computer use during lectures’ . Functional connectivity within and between intrinsic brain networks correlates with trait mind wandering

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• Published: 11 May 2022

On the relationship between mind wandering and mindfulness

• Angelo Belardi 1 ,
• Leila Chaieb 2 ,
• Alodie Rey-Mermet 1 ,
• Florian Mormann 2 ,
• Nicolas Rothen 1 ,
• Juergen Fell 2 &
• Thomas P. Reber 1 , 2  

Scientific Reports volume  12 , Article number:  7755 ( 2022 ) Cite this article

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Mind wandering (MW) and mindfulness have both been reported to be vital moderators of psychological wellbeing. Here, we aim to examine how closely associated these phenomena are and evaluate the psychometrics of measures often used to quantify them. We investigated two samples, one consisting of German-speaking unpaid participants (GUP, n \(=\) 313) and one of English-speaking paid participants (EPP, n \(=\) 228) recruited through MTurk.com. In an online experiment, we collected data using the Mindful Attention Awareness Scale (MAAS) and the sustained attention to response task (SART) during which self-reports of MW and meta-awareness of MW were recorded using experience sampling (ES) probes. Internal consistency of the MAAS was high (Cronbachs \(\alpha\) of 0.96 in EPP and 0.88 in GUP). Split-half reliability for SART measures and self-reported MW was overall good with the exception of SART measures focusing on Nogo trials, and those restricted to SART trials preceding ES in a 10 s time window. We found a moderate negative association between trait mindfulness and MW as measured with ES probes in GUP, but not in EPP. Our results suggest that MW and mindfulness are on opposite sides of a spectrum of how attention is focused on the present moment and the task at hand.

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Introduction

Waking experience can be described as a stream of thoughts, perceptions, and emotions that come in and out of the focus of our conscious awareness. Mind wandering (MW) refers to our thoughts becoming decoupled from an ongoing task and coupled to thoughts and feelings not being subject to the task at hand or our surroundings 1 . In comparison, mindfulness refers to the mental act of intentionally resting the focus of awareness on a particular subject of experience in the present moment without judgment 2 . These constructs appear to emphasise aspects which lie on opposite sides of a spectrum of how intentional, focused, and self-aware one is regarding the thoughts and perceptions that make up one’s conscious experience 3 .

In light of these conceptual considerations, it seems surprising that statistical associations between measures of MW and mindfulness are rather low 3 , 4 , 5 . One possible explanation for this may be the low reliability of the psychometric tools used to measure these constructs. Another possibility could be that meta-awareness of MW 6 , 7 , i.e., awareness of the fact that ones contents of consciousness is decoupling from an ongoing task, moderates the relationship between mindfulness and MW. To investigate these questions, we first assessed the psychometrics of a well-established mindfulness questionnaire and self-report measures of MW and meta-awareness thereof in a large sample from an online study. Then we estimated the associations between measures of MW, mindfulness, and meta-awareness.

Evidence for the importance of both MW and mindfulness for psychological wellbeing has been reported numerous times in the literature. Increased propensity to MW was associated with reduced affect 8 and in its extreme form MW can result in persistent negative and repetitive thoughts leading to rumination. Such rumination is at the heart of neurocognitive models of depression 9 , 10 , 11 . Furthermore, distraction due to MW can potentially cause physical harm e.g. when driving 12 , operating heavy machinery 13 , or when working as a medical professional 14 . Excessive MW may also interfere with career goals by affecting work and educational performance 15 . While a majority of studies focus on negative consequences, MW may also facilitate future planning, goal setting, and aid creative problem solving 16 , 17 , 18 . For example, Medea and colleagues 18 found that self-generated cognition during an episode of MW may allow the development of more concrete personal goals.

In contrast, mindfulness has been associated predominantly with an increased feeling of wellbeing. The concept of mindfulness has its origins in eastern philosophy and is closely linked to processes of awareness and attention. Mindfulness describes a state in which a person willingly chooses the focus of conscious experience and takes constant notice of their contents of consciousness. Practicing to achieve this mindful state has been a central tenet of traditional Buddhist meditation, and has been introduced in western cultures as a secular form of mental practice and flavours in psychotherapy, such as e.g., the mindfulness-based stress reduction (MBSR) program or acceptance and commitment therapy 2 , 19 , 20 .

A widely used task to experimentally elicit MW is the sustained attention to response task (SART) 21 . Participants view, for example, a stream of numbers from 0 to 9 appearing in a random sequence and at a constant rate. The participants’ task is to press a button in response to all non-target digits (Go trials) except for one – the target, where they are required to withhold their button press (i.e., the Nogo trial, such as the number 7). Several dependent variables have been used in the SART, such as the performance of the task (i.e., the error rate on Go trials and Nogo trials), the mean reaction time (RT) in Go trials and the variance of these RTs, as well as scores combining performance and RT (e.g. a skills index, calculated as accuracy/RT) 22 . Variants of the task include querying participants intermittently in defined intervals as to whether their mind was ‘on task’ or ‘off task’ using experience sampling (ES) probes to measure MW. Furthermore, meta-awareness of MW is queried after ES of MW in some studies immediately after ‘off task’ responses 23 , 24 . In addition to the self-reports from ES, low performance 25 , 26 , 27 as well as long and widely dispersed RTs 16 in the SART are considered evidence for low sustained attention and potentially for MW. Several versions of the paradigm combining ES probes and SART have been used in previous research. For example, some studies restricted performance and RT analyses to short time windows immediately preceding appearance of the ES probe 16 , 27 . Other studies varied SART difficulty by either adding auditory noise 28 , by making the number stream predictable 29 , or by increasing the inter-stimulus interval (ISI) 30 . Taken together, there is a variety of ways in which the SART is used to elicit and assess MW.

Tools to measure mindfulness, on the other hand, consist predominantly of self-report questionnaires. One of the most commonly used questionnaires is the Mindful Attention Awareness Scale (MAAS) 31 . Previous assessments of the MAAS found that it has a single factor structure and overall robust reliability (Cronbach’s \(\alpha\) between 0.8 and 0.87) 31 . External validity was evaluated with numerous questionnaires assessing a variety of related constructs such as everyday attention, personality traits and anxiety 31 , 32 . Because of the high importance of this questionnaire in mindfulness research, we explored the possibility of shorter versions of the MAAS, based on only 5 and 3 items, which would be quicker to implement in future research.

Despite the close conceptual relationship between MW and mindfulness, estimates of the strength of their association have been surprisingly low 3 , 4 , 5 . Furthermore, none of these previous studies reported an estimate of reliability for ES of MW, making the interpretation of this association difficult. See Table 1 for a detailed summary of previous findings. Together, there is only weak evidence to suggest that a direct measure of MW such as ES during the SART correlates with MAAS scores. Moreover, when these associations were reported, they were moderate at best.

Additional evidence for the relationship comes from a related line of research that investigates whether mindfulness training impacts direct and indirect measures of MW (for a review, see 33 ). Such intervention studies found that the practice of mindfulness usually improved SART performance 3 , 34 , 76 , 36 , 37 , 38 and reduced the frequency of self-reported MW in some cases 36 , 39 but not in others 34 , 38 . Moreover, one study reported higher MAAS scores after mindfulness training 35 . Similarly to the findings of those correlation studies reported before, in these mindfulness training studies the associations between the direct MW measure and mindfulness is not as strong as one might expect.

One possible explanation of low associations between ES of MW and MAAS scores could be that queryi ng participants for whether they were on or off task alone conflates over two forms of MW that are opposingly linked to mindfulness, namely MW with and without meta-awareness 6 . This hypothesis has been put forward by Smallwood and Schooler 7 , and initial empirical evidence for the importance of considering meta-awareness was gathered by the same authors in an ensuing study 9 . Here, ‘zone outs’ (MW without awareness) were linked to higher inhibition errors in an ongoing task while ‘tune outs’ (MW with awareness) were not. How these ‘zone out’ and ‘tune out’ propensities are linked to trait mindfulness, however, seems unclear in the previous literature. Deng et al. 4 found no significant relationship between either the ‘zone out’ or the ’tune out’ rate with trait mindfulness as measured by the MAAS. A more recent study 5 found both rates to be negatively associated with MAAS scores. Together there is inconsistent evidence on the role of meta-awareness as potential mediator between MW and trait mindfulness. Another possible explanation for low correlations between SART, ES, and MAAS is insufficient reliability of measures derived from these instruments. Reliability is an often overlooked quality metric in cognitive tasks while it is routinely reported for questionnaires 40 . Reliability estimates are important as they determine an upper limit of how large correlations between two measures can be. For all the individual measures for mindfulness and MW discussed above, robust psychometric properties have been reported before, though rarely combined and sometimes in small samples: MAAS 31 , 32 , specific SART measures with and without ES for MW 41 , 35 , 36 , 37 , 45 . Table 1 lists all referenced studies that measured the MAAS and/or the SART with or without ES of MW. The table depicts sample sizes, reliability estimates and estimates of association. Most importantly, this table shows that none of the previous studies employed all three measures (MAAS, SART, and ES of MW) and reported both, reliabilities of all measures as well as correlations between all of them. The present study aims to fill this gap and offers data from two new large samples.

Overall the aim of this study is to assess the psychometric quality of several measures for MW and mindfulness from the SART, MAAS, ES of MW and ES of meta-awareness. In a second step, we want to gain an estimate for the statistical association between these constructs. We combined ES of MW during the SART with an established measure of mindfulness in an online study in two large samples collected in an online experiment and by doing so add psychometric estimates for these measures gained in an online study and assessed together.

We recruited two samples of participants for a German and an English version of the experiment. In our first recruitment phase we targeted German-speaking participants through the participant pool of our institution, made up of students and volunteers from the public. Throughout the study, we refer to this sample as German-speaking unpaid participants (GUP). In a second phase, we recruited and paid participants predominantly through Amazon Mechanical Turk (AMT, mturk.com) for an English version of the experiment. We refer to this sample as English-speaking paid participants (EPP). All participants first answered a questionnaire on demographics and the MAAS, then they performed a 20 min version of the SART during which ES probes of MW and meta-awareness wee obtained (see “ Methods ” section).

Sample differences

We initially planned to report our findings as one sample, since the online experiment was identical except for the language. However, after finding significant differences between our two samples in the SART and ES data, we decided post-hoc to report all findings separately for GUP and EPP (see Table 2 for sample differences between all main measures). Most strikingly, EPP reported significantly less than half as often to be ‘off task’ than the GUP \((\hbox {t}(519.01) = -10.06\) , p < .001, d \(=\) 0.81, \(M_E{}_P{}_P = 0.09\) , \(M_G{}_U{}_P = 0.25\) ). This indicates much lower variance in ES data in the EPP. There were also significant differences on all measures derived from the SART directly (RT, accuracy) albeit in a lower magnitude (see Table 2 ).

Factorial structure and reliability of the MAAS

We first checked the correlation matrices of the individual items on the questionnaire and the total score, separately for each of the two samples. In the GUP sample, item 6 had low item-to-total correlation (r \(=\) 0.05) and correlations below r \(=\) 0.2 with most other items. For that reason, we excluded item 6 from further analyses for the GUP. Thus, our total MAAS score for the EPP contained all 15 initial items, while the score of the GUP contained only 14 items.

We then conducted an exploratory factor analysis (EFA) for the MAAS responses for each of the two samples (factor loadings for one-factor EFAs are presented in Table 3 ). Figure 1 depicts scree plots for the EPP and GUP; these plots suggest that a single latent factor drives responses in the MAAS. Further EFAs also revealed that two-factor models only explain little additional variance (EPP: 3% and GUP: 5%), in comparison to that explained by one-factor models (EPP: 63% and GUP: 36%). However, the Kaiser rule (selecting the factors with an eigenvalue above 1; indicated by the dotted line in the scree plots) is also in accordance with a two-factor solution in our GUP.

The model fit statistics from confirmatory factor analyses (CFA) were estimated using the Comparative Fit Index (CFI), the Tucker Lewis Index (TLI), and the Root mean square error approximation (RMSEA). We compared the values against common standards for an acceptable fit (CFI/TLI > 0.9, RMSEA < 0.06) 52 . For one-factor models, the fits are acceptably high in the EPP (CFI \(=\) 0.954, TLI \(=\) 0.946). The fits were poorer, however, for the GUP (CFI \(=\) 0.858, TLI \(=\) 0.832). The RMSEA, which is an absolute fit statistic, indicates a poor approximate fit for both models, in the EPP (RMSEA \(=\) 0.08) and GUP (RMSEA \(=\) 0.096). However, the use of a fixed threshold for the RMSEA is questionable 53 , 54 . The full fit statistics of these two models and of an alternative two-factor model for the GUP can be found in the supplementary materials at https://osf.io/8kg6z . Together, EFA and CFA are mostly consistent with the notion of one single factor driving responses to the MAAS, even though some fit statistics for the CFA were below the threshold for an acceptable fit.

Reliabilities of the MAAS score (mean of individual items) were overall high. For the full MAAS the standardized Cronbach’s \(\alpha\) was 0.88 in the GUP sample and 0.96 in the EPP. We created and then investigated shorter versions of the questionnaire consisting of the three or five items with the highest loadings in the EFAs. In the EPP these items were 7, 8, 10, 1, 11, and in the GUP items 14, 8, 9, 10, 7, in order of decreasing loading (see also Table 3 ). We refer to these shortened scales as the MAAS-5 and MAAS-3. The Cronbach’s \(\alpha\) s of the scales are given in Table 4 and further descriptives of the scores are available in the supplementary materials (Table S7 ). Correlations between short and full MAAS scores were reasonably high (between r = 0.79 and r = 0.97, see full correlation matrices in the supplementary materials (Figs. S9 and S10 ).

Scree plot for MAAS for EPP and GUP samples. This figure was created using R (v. 4.02) 55 with package ‘ggplot2’ (v. 3.3.5) 56 .

Reliability of MW measures taken from the SART and ES

Estimates of reliability of the measures derived from the SART and ES probes are presented in Table 4 . They are split-half reliabilities derived using a permutation-based approach with 5000 random splits 40 , 57 . For further descriptives of the measures, see Table S7 in the supplementary materials. From the SART, we report these measures: accuracy, the mean (M) and standard deviation (SD) of RTs during all trials and also in only those trials preceding the ES probes within a 10-s time window, a measure used in MW neuroimaging studies 27 . SART values are reported separately for correct Go trials and incorrect Nogo trials. From ES probes, we report the proportion of all ES probes in which participants answered that they were off-task (Attention Off) and the proportion of meta-awareness probes in which participants answered that they were unaware that their attention was off task (Meta-Awareness Off). The sample sizes for the meta-awareness probes were smaller, because they exclude participants who reported that they were always on task. Split-half reliabilities for measures from Go trials in the SART and for ES probes are generally high. Reliabilities for Nogo trials were markedly lower, and were further reduced when restricting the analyses to the 10-s time windows immediately preceding ES probes. It is noteworthy that the sample sizes varied for these different measures due to the structure of the data and restrictions for the split-half calculations: Each participant needed at least four valid data points for the split-half procedure, as each split required two data points to calculate a mean or standard deviation. Furthermore, only 10.6% of all trials were Nogo trials and participants only reacted to 15.2% of Nogo trials, making Nogo trials with participant reaction somewhat scarce.

Estimates of association between the MAAS, SART, and ES

In a next step, we assessed the hypothesized negative association of MW with mindfulness. To this aim, we correlated measures derived from the SART and ES with the MAAS (Fig.  2 ). For the link between the direct measure of MW and mindfulness, we found ES probes (Attention Off) were moderately negatively associated with the MAAS in GUP ( \(\hbox {r} = -.29\) , \(p< 0.001\) ) but not in EPP (r \(=\) 0.04, \(p > 0.1\) ). Between indirect measures of MW and mindfulness, there was no indication for an association between the SART and the MAAS in GUP. In EPP, however, there were small correlations between MAAS total score and SD of RTs in the Go trials during the 10 s window before ES probes ( \(\hbox {r} = -.23\) , \(p < 0.05\) ), between MAAS total score and accuracy in all Nogo trials ( \(\hbox {r} = .13\) , \(p < 0.05\) ), and a medium association between MAAS total score and accuracy of Nogo trials in the 10 s window before ES probes ( \(\hbox {r} = -.43\) , \(p < 0.01\) ). The pattern is mostly consistent with the idea of a negative association of MW and mindfulness. There was no association between meta-awareness probes and MAAS scores in both samples. All pairwise correlations for both samples are available in Tables S1 and S2 in the supplementary materials at https://osf.io/8kg6z .

To check whether these correlations might have been heavily influenced by outliers or non-normally distributed data, we additionally bootstrapped the correlation coefficients and 95% confidence intervals (CIs) for these pairwise correlations (1000 iterations, 100 random participants sampled in each). In addition, we compared the Pearson product-moment correlations to Spearman rank correlations. These analyses showed a similar pattern of results from the Pearson correlations reported above in the GUP, but in the EPP the three reported associations with ES probes were not significant in the Spearman correlations. This further indicates the different answer patterns in self-reported MW between our two samples. The detailed results of these additional versions of the correlations are available in Tables S3 – S6 in the supplementary materials.

Pairwise Pearson correlations for MAAS, SART, and ES measures. Correlation coefficients are reported for whole sample (‘Corr’), and for EPP and GUP samples separately. Individual plots below the diagonal are scatter plots with regression lines for the two variables intersecting at this cell, those on the diagonal show density distribution plots for the two samples. Significance markers: . \(=\) \(p< 0.1\) , * \(p< 0.05\) , ** \(p< 0.01\) , *** \(p< 0.001\) . This figure was created using R (v. 4.02) 55 with packages ‘ggplot2’ (v. 3.3.5) 56 and ‘GGally’ (v. 2.1.2) 58 .

This study entailed between-subject manipulations hypothesized to affect MW that are out of the scope of the current work. Briefly, we investigated whether exposing participants to auditory stimuli (5 Hz monaural or binaural auditory beats, silence, 440 Hz sine tone) could reduce their propensity to MW. Since such a finding has been reported earlier, in particular for participants exhibiting high proportions of MW 24 , we experimentally manipulated the occurrence of MW in three different ways. First, we varied the inter-stimulus-interval (1 vs. 2 s). Second, we implemented the stimuli in the SART predictably or unpredictably. Third, a creative problem-solving task was executed for a second time after the SART, and participants were either informed before the SART about the second execution or they were not informed.

These between-subject manipulations may have affected our estimates of associations between MW and mindfulness. To investigate this possibility, we first calculated ANOVAs with the experiment’s main manipulations (and all pairwise interactions) as predictors and measures from SART and ES as outcome variables. We then added the MAAS score as covariate to these, to create a set of comparable ANCOVAs. To evaluate whether our associations were affected by the experimental manipulations, we then checked two things. First, we compared the effect sizes ( \(\eta ^2\) ) of the total MAAS score in these ANCOVAs with the coefficient of determination ( \(r^2\) ) between the MAAS score and SART and ES measures. Second, we calculated model comparisons between the ANOVAs and ANCOVAs using likelihood-ratio tests (Table 5 ).

The effect sizes were for most combinations very similar in the correlations and the ANCOVAs. In all but one case, adding the MAAS score as covariate did not significantly improve the model fit. Only in the ES MW variable in GUP did adding the MAAS score as covariate significantly improve the model fit. There the estimate of association between ES MW and the MAAS score slightly increased when accounting for experimental manipulations. This result provides confirmatory evidence that MAAS and ES MW are weakly negatively associated in the GUP sample.

We examined the psychometrics of MW, meta-awareness of MW, and trait mindfulness, as well as the associations between these constructs. Overall, we found reasonably good psychometrics of all measures, and evidence that MW and trait mindfulness are indeed moderately negatively correlated. This association was not moderated by meta-awareness of MW. Neither the psychometrics nor moderating effects of meta-awareness can therefore readily explain that associations between MW and mindfulness are of a rather low magnitude.

In keeping with previous studies, we found overall good psychometric properties and evidence mostly consistent with a single-factor structure for the MAAS questionnaire. Our estimates of reliability of the MAAS were slightly higher than those reported in earlier studies, in both the EPP and GUP. For the English MAAS, the original publication reported internal consistencies in the range of [0.8, 0.87] 31 , and a further study reported 0.89 48 , but this value was 0.96 in our EPP. For the German MAAS, a Cronbach’s \(\alpha\) of 0.83 was reported in the initial publication on the psychometric properties of the questionnaire 49 , while the value in our GUP was 0.88. Very high internal consistencies might indicate redundancy in a questionnaire, suggesting some items are superfluous and can be removed, which would lead to a more efficient assessment 59 . Results on the proposed shorter versions of the MAAS (MAAS-5 and MAAS-3) outlined in this study support this notion and may provide researchers with tools to optimize data collection.

One peculiarity we observed in the MAAS data for the GUP was item 6 ( “I forget a person’s name almost as soon as I’ve been told it for the first time.” 31 ), which correlated very poorly with all other items and the total score. Interestingly, the authors of the German MAAS also observed complications with this item but decided to include it to ensure international comparability 49 . Specifically, they found an item-to-total correlation of r \(=\) 0.18 for item 6 while the next-lowest correlation was for item 1 (r \(=\) 0.26) and those for all other items ranged from 0.33 to 0.65 We did not observe, however, such a low item-to-total correlation of item 6 in EPP. Nevertheless, we assume that cultural differences or mere issues related to translation cannot account for low item-to-total correlation for this item, as it was also observed in a study with English-speaking participants from New Zealand 50 . Moreover, item 6 was also one of the most poorly correlated items in the original English article detailing the MAAS 31 . We suggest item 6 may only occasionally be problematic as its meaning is ambiguous, and can be understood in two different ways. First, it could—probably as intended by the authors of the scale—measure attention usually directed to a person introducing themselves, or it can be understood as asking for self-report on one’s long-term memory abilities, which is arguably an altogether different trait than mindfulness.

While reliability is routinely reported for questionnaires such as the MAAS, they are less common for cognitive behavioral measures, e.g. for the MW measures derived from the SART and ES 40 . Still, earlier studies generally reported high reliabilities also for the SART: e.g. between 0.83 and 0.89 for overall accuracy in the SART 42 , 44 , between 0.92 and 0.98 for SDs of RT 44 , 45 , and even as high as 0.94 to 0.98 for the accuracy of Nogo trials 41 (see Table 1 ). Some of these studies, however, used a shorter stimulus-onset asynchrony (SOA) and much smaller sample sizes (13 42 and 12 41 participants). Also, earlier studies reporting SART reliabilities were usually laboratory studies with more controlled environments. These factors might have led to even higher reliabilty estimates for measures derived from Nogo trials. Our study adds further reasonably high reliabilities with alphas ranging from 0.84 to 0.99, on measures derived from the Go trials of the SART. In contrast to previous studies, reliability estimates for measures derived from Nogo trials were markedly lower (between 0.24 and 0.71) in our samples. These were probably low in our study due to only a small fraction of the SART trials that can be used to derive these measures as we increased the SOA from the original version in order to foster MW. Overall reliabilities are further reduced when restricting the analyses to a short time window preceding ES probes. Filtering the usable trials to a specific time window seems predominately appropriate for neuroimaging studies looking to isolate brain activity patterns of MW, which is where this analysis strategy originated 27 . Researchers focusing on Nogo trials and segmenting the data accordingly, should therefore take care to ensure that the number of trials analyzed remains reasonably large, and bear in mind that reliability of measures derived with these strategies is likely limited. Our reliability estimates for the ES MW probes during the SART (0.91 in GUP and 0.89 in EPP) were within the range of what earlier studies reported (e.g., 0.89 43 and 0.93 45 ). Together with the reliability estimates of the MAAS, our study demonstrates that high reliabilities of the MAAS, SART, and ES for MW can also be obtained in an online study setting.

Our results also stress notions of caution related to recruiting participants via crowdsourcing platforms such as—as in our case—Amazon Mechanical Turk (AMT, mturk.com). We noticed that the two samples behaved differently in the SART and ES, in that AMT participants (the EPP) were less likely to respond that their attention had been ‘off task’ but at the same time showed lower accuracy rates and slower, more varied RTs during the SART. This is likely to have also affected the estimate of association between self-reports of MW in ES probes and the MAAS score. A significant correlation was found in GUP, but not in EPP. The absence of a significant correlation could be due to lower variance in the ES probes of EPP versus GUP. We suggest the different patterns of results relating to the ES probes is not simply due to cultural or language differences, but rather due to differences in motivation to participate. Requesters at AMT are allowed to withhold payment if they are not satisfied with the performance of the participant. It thus seems reasonable to assume that some participants recruited through AMT reported being on task even when they were not. Our data underlies arguments made earlier that caution is warranted when recruiting via AMT and similar platforms, especially when using measures that are susceptible to the issues discussed above 60 , 61 . It might help to explicitly ensure participants that they will experience no disadvantages when they report being off task.

Our results support the hypothesis of a negative link between trait mindfulness and MW. Associations, however, were scattered over different measures and differed between our two samples: There was a moderate correlation of the MAAS with the self-report measure of MW (ES probes during the SART) in one of our samples (GUP) and with SART SDs of RTs and SART accuracy in the SART in the other sample (EPP). Low and absent associations between MW and mindfulness cannot be explained by low reliabilities of the measures we used, as reliabilities were generally satisfyingly high, with the exception of measures derived from SART Nogo trials. With that in mind, the associations based on measures with high realiabilities are only two: that between MAAS total score and ES MW in the GUP, and between MAAS total score and SDs of RTs in SART Go trials during the 10 s window before ES probes in the EPP. One potential explanation for finding the clear association between MAAS and ES MW only in the GUP might be a lack of variance in the EPP data as mentioned above. The lack of variance was due to a large proportion of participants who answered that they were rarely or never ‘off task’ during the experiment.

Despite good psychometrics of our measures, the link between trait mindfulness and MW was only moderate. A further explanation for rather low associations could be that meta-awareness of MW moderates the hypothesized associations. Our finding that meta-awareness of MW is not linked to mindfulness goes against such a hypothesis and some empirical evidence 7 , 23 . However, our results are in accordance with more recent papers that also do not find a moderating effect of meta-awareness on the association between MW and mindfulness 4 , 5 . Nayda et al. 5 reported negative associations between both, the propensity to ‘tune out’ (meta-aware MW) with mindfulness, and the propensity to ‘zone out’ (meta-unaware MW). An earlier publication by Deng et al. 4 found insignificant correlations between trait MW and both ‘zone out’ and ‘tune out’ propensities. It seems noteworthy that both correlations of the Deng et al. 4 study are in the same range and direction as in Nayda et al. 5 but do not reach statistical significance likely due to the low sample size (N \(=\) 23). A potential caveat here is that these rates are calculated using the total of MW probes, rather than the total of meta-awareness probes only. These estimates are therefore biased in that the sum of the ‘tune out’ and ‘zone out’ rates is perfectly inverse proportional to the ‘on-task’ rate. In our analyses, we calculated the meta-awareness rate as proportion of the total of meta-awareness probes instead of the total of MW probes. We found no significant correlation between meta-awareness of MW and mindfulness. Thus, further research seems needed to isolate a potentially moderating effect of meta-awareness on the correlation between MW and mindfulness.

A further reason for low associations between MW and mindfulness could result from the difference in the trait versus transient nature of the constructs. Mindfulness is conceived and measured as a general personality trait. However, MW is a much more transient and fluctuating phenomenon during an ongoing and often boring task. Moreover, boredom itself may explain the low associations between MW and mindfulness. In MW research, the SART is often chosen as an ongoing task, because it is boring and therefore is thought to facilitate MW. The notion that boredom is an enabling factor for MW is supported by two findings. First, boredom has been shown to correlate with attentional lapses as measured with the SART 62 . Second, positive correlations between boredom and MW have been recently reported 63 . In contrast, when participants respond to the mindfulness questions of the MAAS, it is unclear to what extent participants consider boring ongoing tasks (e.g., “I rush through activities without being really attentive to them.” see Table 3 for the complete list of items of the MAAS). Therefore, while boredom seems a relevant aspect of MW when measured with the SART, this is not assessed with the MAAS. Together, this emphasizes the necessity of investigating the role of boredom in the relation between MW and mindfulness in future studies.

One may argue that a further reason for low associations between MW and trait mindfulness could be that the on-task state is more heterogeneous than previously thought. Heterogeneous on-task states were identified by assessing ongoing thought with multidimensional experience sampling (MDES), i.e., extending ES with several questions inquiring about the thoughts’ content and nature 64 . Principal component analysis (PCA) of MDES data revealed several components taxing into the on-task state, which were associated with distinct neural correlates 65 , 59 , 60 , 68 . One component was related to self-focused off-task thoughts while another component indicated detailed task focus. This task-focused component was common in cognitively demanding tasks like tasks measuring working memory, task switching, and gambling. However, it was less observed in low-demand tasks like the SART, where self-focused off-task thoughts prevail 64 . Together, these studies suggest that being more mindful might be linked to how people engage with tasks, perhaps by doing so in a more focused way. The possibility of multiple on-task states may therefore, contribute to the relatively low estimate of the association between mindfulness and ongoing thought.

Finally, low associations between MW and mindfulness could be due to insufficient validity, rather than reliability of the measures we used. While our current study focuses on reliability others have focused on issues related to validity, especially concerning the questionnaires to measure mindfulness 69 . On the one hand, the MAAS in particular has been shown to correlate reasonably well with other questionnaires measuring mindfulness such as the Five Facet Mindfulness Questionnaire (FFMQ) 70 . Further evidence for converging validity with, e.g., positive affect or attention, as well as evidence for discriminant validity, e.g., with anxiety and rumination, has been found in studies reporting correlations with MAAS scores 31 , 32 . On the other hand, questionnaires rely on introspective capabilities and may be subject to bias. A recent study by Isbel et al. 70 questioned especially the discriminant validity of the MAAS and the FFMQ as these measures increased following both a mindfulness training intervention and a control training intervention not aimed at mindfulness. Rather, objective accuracy of breath counting has been found to respond selectively to the mindfulness training intervention 70 . A potential reason why the breath counting task responded selectively to the mindfulness training is that mindfulness training itself often consists of exercises to guide one’s attention specifically on the breath. It is hence a rather near transfer from mindfulness training to an increase in accuracy in breath counting. Nevertheless, more research exploring the practical validity of mindfulness measures is required.

Recent methodological developments in MW research highlight limitations in our findings and offer advice for future research. Contemporary studies of ongoing thought that utilized MDES show that different tasks used in MW research elicit several distinct thought patterns to varying degrees 64 , 67 . Our study is consequently limited by the fact that we only used the SART to investigate the association between individual variation in mindfulness and MW instead of several tasks. The SART also has the limitation that it does not lead to much detailed task focus and tends to stimulate self-focused MW 64 . Due to that, it is unclear whether our findings generalize to other tasks or whether they are specific to the SART and thus to those types of ongoing thoughts more likely to be evoked by the SART like self-focused MW.

Besides the heterogeneity of ongoing thoughts, the relationship between MW and mindfulness is likely modulated by various other factors. A recent study has highlighted MW as a complex phenomenon that warrants a multi-faceted approach that includes a) dispositional traits, like conscientiousness, agreeableness, or mindfulness, b) contextual factors, like motivation or alertness, and c) cognitive abilities, like working memory capacity 71 . If the relationship between MW and mindfulness is embedded within such a multi-faceted approach, the association between these two factors might be diluted by other potential confounding factors that were not accounted for. In this regard, future research will benefit from assessing MW and mindfulness with a broad set of tools including MDES and multiple tasks with variable demands that elicit different patterns of ongoing thoughts.

Participants

A total of 715 participants performed or started our online experiment between October 2019 and January 2021. We excluded participants from the data analysis for these reasons and in this order: Repeated participation (n \(=\) 11), incomplete data due to technical issues (n \(=\) 1), incomplete or delayed participation in the experiment (time in experiment < 23 min or > 120 min [n \(=\) 59]), low number of correct SART trials (proportion of correct Go trials < 2/3 [n \(=\) 51], or proportion of correct Nogo trials < 1/2 [n \(=\) 22]), and outliers who took a long time to answer the ES probes (n \(=\) 30). For this last point we established a cutoff based on the interquartile range (IQR) due to the highly skewed distribution of these values. Cutoff was the 75th percentile plus three times the IQR. We based our data analyses on a total sample of 541, separated into 313 GUP (aged between 16 and 85, M \(=\) 38.78, SD \(=\) 12.95) and 228 EPP (aged between 19 and 68, M \(=\) 34.27, SD \(=\) 11.39). Further demographic characteristics are given in Table 6 .

We recruited participants for two different language versions of the experiment through various routes. The GUP (n \(=\) 313) consists of: (a) 97 participants recruited by the students of two classes in the autumn 2019 and spring 2020 semesters at UniDistance Suisse; (b) 200 students and members of the public interested in participating in experimental research from our institute’s pool of research participants; and (c) 16 participants who followed links in an information email to university employees and on different websites. The EPP (n − 228) contains: (a) 217 participants recruited through AMT, (b) 10 who were PhD students at the Department of Epileptology at the University of Bonn, and (c) 1 who followed a link from an external website.

Those recruited through AMT were paid USD 3.50 when they had completed the whole experiment. Students in the Bachelor’s program in Psychology at the UniDistance Suisse received course credits for their participation. Other participants received no compensation. All participants gave informed consent by reading information provided online and then checking off tick boxes in an online form before they proceeded to the experiment. The study was carried out following all the relevant guidelines and regulations. The study and its compliance with relevant guidelines and regulations was approved by the ethical review committee of the Faculty of Psychology at UniDistance Suisse ( https://distanceuniversity.ch/research/ethics-commission/ ). In particular, all procedures are in accordance with the Declaration of Helsinki.

The data reported here was collected in a study also investigating the effects of experimental manipulations on MW. Participants performed the SART with intermittent ES probes to directly obtain self-reports of episodes of MW. These experimental manipulations are outside the scope of the current work as they focus on potential effects of auditory beat stimulation on MW 24 , 72 and will be reported elsewhere. Briefly, experimental manipulations were performed in a \(4\times 2\times 2\times 2\) between-subjects design and included the independent variables Auditory Beat Stimulation (5 Hz binaural, 5 Hz monaural; 440 Hz pure tone; no sound), SART ISI (1 or 2 s), Predictability of the Number Sequence in the SART (random or ascending), and Expectancy of an ensuing creativity task (expected or unexpected). Dependent variables are RTs and Accuracy during the SART and ES MW probes. It was for the purpose of this study, that we collected data using the MAAS.

Instruments

To assess trait mindfulness we applied the MAAS, a 15-item questionnaire that determines attention to the present in everyday experiences 31 . For the German version of the experiment, we used the validated German translation available from the Leibniz Institute for Psychology Information (ZPID) 73 .

To measure MW indirectly through lapses in sustained attention in a deliberately monotonous task, we used the SART 21 . The SART is a Go/No-go task that uses digits as stimuli which are presented individually on screen with a fixation cross shown between stimuli. Participants are asked to react to all digits (Go trials) except for the number 7 (Nogo trials). We adapted the original SART with the intention to make it more monotonous, in order to elicit more MW. Specifically, we displayed each stimulus longer (2 s instead of 250 ms), had a longer ISI (1 or 2 s instead of 900 ms), and used a fixed font size (instead of randomly varying font sizes) to present our stimuli 21 .

We assessed self-reported MW using ES probes during the SART. In intervals between 25 and 35 s, participants were asked: “Immediately before this question appeared, was your attention focused ON the task or OFF task?” with a dichotomous forced-choice answer. When “OFF task” was selected, a second question appeared: “Were you aware that your attention was OFF task?” with a dichotomous forced choice answer again (yes or no). There was no time limit to answer these probes.

To further increase MW by adding a cognitive distraction during the SART and to assess particpants’ creativity, we implemented a short task for divergent thinking based on the alternative/unusual uses concept originally introduced by Guilford 74 . In this unusual uses task (UUT), participants were given 20 s to find alternative uses for a brick, with the original use described as “building houses”. Participants entered their answer in a large text field and were asked to enter one answer per line.

We implemented the MAAS and SART with ES as an online experiment using the JavaScript-based online experiment builder “lab.js” ( https://lab.js.org 75 . Participants were required to wear headphones during the experiment. We included a headphone test before the SART to ensure participants had correctly placed the headphones and could listen to the stimulation. Runnable files and code for both language versions of the experiment can be found in the supplementary materials at https://osf.io/8kg6z .

The online experiment started with information about the experiment, data processing, and informed consent request. This was followed by a short demographic questionnaire, the MAAS, the headphone test, the UUT, and 20 min of the SART. After the SART, a summary page informed the participants about their performance and a debriefing page gave further background information about the study.

Data processing, analysis and creation of figures and tables were done in R (v 4.0.2) 55 , using the following packages in addition to base R: ‘tidyverse’ 76 for various data wrangling and processing tasks and for data visualizations via ‘ggplot2’ 56 , ‘GGally’ 58 for data visualizations, ‘e1071’ 77 for kurtosis and skewness calculations, ‘lubridate’ 78 for handling of date and time data, ‘lavaan’ 79 for confirmatory factor analyses, ‘stargazer’ 80 to create and export LaTeX tables, ‘splithalf’ 57 for permutation-based split-half calculatio ns.

Data availability

The datasets generated and analysed for the current study are available in the Open Science Framework (OSF) repository, https://osf.io/wg9q5 . Tables, figures, and other supplementary materials specifically for this publication are available in a different repository at OSF, https://osf.io/8kg6z .

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Acknowledgements

The authors would like to thank all students of the following two classes at the UniDistance Suisse, who recruited participants for the experiment: “Methoden III: Experimentelle Übungen” during the fall semester 2019, “Wissenschaftliches Arbeiten” during the spring semester 2020.

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Angelo Belardi, Alodie Rey-Mermet, Nicolas Rothen & Thomas P. Reber

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Using the CRediT contributor roles taxonomy (casrai.org/credit/). A.B.: Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Project administration, Software, Validation, Visualization, Writing—original draft, Writing—review & editing. L.C.: Conceptualization, Resources, Writing—review & editing. A.R-M.: Conceptualization, Writing—review & editing. F.M.: Resources, Writing—review & editing. N.R.: Resources, Writing—review & editing. J.F.: Conceptualization, Writing—review & editing. T.P.R.: Conceptualization, Resources, Formal analysis, Investigation, Methodology, Project administration, Software, Validation, Supervision, Writing—original draft, Writing—review & editing.

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Belardi, A., Chaieb, L., Rey-Mermet, A. et al. On the relationship between mind wandering and mindfulness. Sci Rep 12 , 7755 (2022). https://doi.org/10.1038/s41598-022-11594-x

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Mind Wandering Can Be a Good Thing

Staying focused is important for nearly every human activity, yet we often struggle to do it. When we are unable to focus our thoughts, we say that we are mind wandering. Mind wandering is very common and occurs in every healthy mind. In fact, mind wandering may even reflect the regular way of thinking, unless people make special efforts to prevent it. But is all mind wandering the same? Why does the mind wander, and when? What effect does mind wandering have in our lives? In answering these questions, we will show how mind wandering can even be helpful for things like creativity and learning.

Mind Wandering and Its Consequences

Any student knows that it can be hard to keep attention focused. For instance, when you are supposed to be listening to your teacher, you may find your mind drifting away. You might look out the window, make plans for after school, or think about nothing at all! Sadly, if students let their attention drift too far or for too long, they may miss what the teacher is saying—much to the dismay of teachers everywhere!

This experience is very interesting to scientists, many of whom also struggled to focus in school. Sustained attention is the term used to describe the ability to keep focused on whatever activities we are trying to do. We know that sustained attention is very important for many different things—like learning and remembering. We also know that sustained attention often fails and attention shifts to unrelated thoughts—this is called mind wandering [ 1 ]. Mind wandering is surprisingly common. Some studies find that people may spend nearly half their day mind wandering.

The effects of mind wandering can vary a lot. Sometimes there are no effects at all ( Figure 1 ). Think about drinking a glass of water: this task is simple and happens often, allowing you to drink without much effort or spilling, even if your mind is wandering. This kind of behavior is automatic.

• Figure 1 - Mind wandering can occur anytime, anywhere—it is a normal part of the way the brain works.
• Photo by Vanessa Bumbeers on Unsplash .

Other times, mind wandering has minor effects. If you briefly lose focus on your teacher’s voice, you may not hear what was said; but by rapidly focusing on the teacher’s voice again, you can get back on track fairly easily. Finally, there are instances when mind wandering can have very serious results. Imagine crossing the street or riding a bike without focusing on your surroundings.

Because mind wandering is such a common and normal part of daily life, scientists have asked two major questions about it. First, is mind wandering one thing, or are there different kinds? Second, why does mind wandering happen at all?

Question #1: Is All Mind Wandering the Same?

Many studies have tried to discover whether there are different kinds of mind wandering. These studies show that people can lose focus in different ways. Mind wandering can happen on purpose or by accident. Attention can also focus inward (on your thoughts) or outward (on the world around you). Finally, people can lose focus just a little (shallow) or a lot (deep). Do not worry if those sound complicated—we will discuss each one.

The first big difference is whether mind wandering is on purpose or not. Most mind wandering appears to happen on its own, or by accident [ 2 ]. For instance, a surprising sound may capture your attention. Other times, you may just lose focus and have no idea why. That said, mind wandering can also happen on purpose. Consider waiting at a doctor’s office, when you must maintain enough awareness to hear your name being called. At the same time, you will probably allow other thoughts to run through your mind. This “on-purpose” kind of mind wandering is common when doing something easy, or when you do not feel motivated.

Another way of understanding mind wandering is to consider what you are thinking about when you lose focus. This is the difference between internal and external mind wandering [ 3 ]. Perhaps while waiting at the doctor’s office, you start looking out the window to watch people walking by—this focuses on your senses and the world around you and is called external mind wandering . The opposite would be if you focused on your inner thoughts—maybe remembering your last doctor’s visit or planning for what you will do later in the day—and this is called internal mind wandering .

Finally, mind wandering can differ based on how deep vs. shallow it is. One idea [ 4 ] is that there are three levels of mind wandering. The deeper your level of mind wandering, the less connected you are to the world around you. Think of mind wandering as a slinky bouncing down stairs. Unless something stops it, the mind will keep going from shallow mind wandering (the top steps) into the deeper kinds (bottom steps).

The first, most shallow step in mind wandering involves very short and shallow dips in your attention to detail. This is relatively common, like briefly zoning out during class. The effects, however, are usually small. People will usually notice they are mind wandering and choose to refocus their attention.

If attention is not refocused, it is likely that mind wandering will progress to the second, medium, level. This involves longer-lasting lapses in attention, which you are less likely to notice. When mind wandering at this medium depth, you can still go through the motions of activities that are familiar to you, like brushing your teeth or eating a meal. These activities are a kind of automatic responding—like a robot that is programmed to do some task but is not really thinking. When the robot makes a mistake, it continues with whatever it was programmed to do.

The final and deepest level of mind wandering involves paying the least attention to the surrounding world. It is marked by the most extreme changes in behavior, like blank stares and missing what others say. In this deeper level, attention is directed internally, or to nowhere at all, which is called mind blanking . This level is most likely to result in serious consequences, like if you are riding a bike or learning to drive ( Figure 2 ).

• Figure 2 - Mind wandering can be dangerous, depending on the activity you are participating in.
• For example, failure to maintain focus while biking could lead to a crash. Shallow mind wandering is less risky (you can likely refocus) but deeper mind wandering is far more dangerous. Photo by William Hook on Unsplash .

In sum, different kinds of mind wandering exist. Despite these differences in the types of mind wandering, a common finding is that people struggle with whatever they are doing when the mind wanders [ 5 ].

Question #2: Why Does Mind Wandering Happen?

Much evidence suggests that mind wandering is not a rare mistake, but actually a very normal part of the way the mind works! In other words, the mind will naturally wander unless it is given a specific job [ 2 , 6 ]. In fact, we now know that attention-related disorders like ADHD can be understood as a normal behavior (mind wandering) that is simply happening in an unusually high amount. This knowledge makes it easier to study how much mind wandering is normal and how mind wandering can impact other parts of life, such as emotions and learning [ 7 ].

So why do we mind wander in the first place? The likely reason is that mind wandering serves useful purposes. For instance, mind wandering can help in problem solving, creative thinking, and planning for the future [ 8 ]. Even when you are not trying to think about anything, your mind is still working in the background. Without trying, your mind might start focusing on memories that could help solve a problem in the present. This can be when creative or unusual ideas are made! For instance, a musician might combine different melodies to make something new.

Also, mind wandering can help with learning and memory—specifically for things that are not relevant to the task at hand [ 5 , 8 ]. People who mind wander more show greater learning for this irrelevant information, and the learning is best during periods of mind wandering [ 9 ]. After learning, mind wandering helps strengthen recent memories. This benefit is strongest when the memories are relevant to you personally.

Finally, mind wandering offers a time to “rest” and prepare for upcoming thinking [ 8 , 10 ]. It prevents new information from entering the mind and using up limited attention in processing that new information. When our minds wander, we can then process older information in new ways. Creative ideas can be built and used to plan or solve problems. When our minds wander, our attention can also focus on sources of information that are potentially useful, like thinking about plans for later in the day, for example. When that information is useful, it can be processed and remembered.

The mind actually does a lot when it wanders! So, do not see mind wandering as a mistake. Try to remember how mind wandering redirects your focus. This allows you to learn new things and to process information better. There are times when you should try to focus your attention, like when riding a bike. However, always remember that taking a mental break is healthy. There are many wonderful things in the world that you can notice when you let your mind wander. So, let yourself gaze out the window or stare at clouds, or even close your eyes and simply “be”.

Sustained Attention : ↑ The ability to focus attention while ignoring distractions, over time.

Mind Wandering : ↑ Thinking about anything other than the task you should be focusing on.

External Mind Wandering : ↑ Focusing attention on the world around you, through your senses (sight, sound, and more).

Internal Mind Wandering : ↑ Focusing attention on your inner thoughts, such as recalling memories or planning for the future.

Mind Blanking : ↑ When the mind is not active, and attention is not focused on any particular thoughts.

ADHD : ↑ Attention Deficit Hyperactivity Disorder; a mental health disorder involving many instances of mind wandering.

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

[1] ↑ Cheyne, J. A., Solman, G. J. F., Carriere, J. S. A., and Smilek, D. 2009. Anatomy of an error: a bidirectional state model of task engagement/disengagement and attention-related errors. Cognition 111:98–113. doi: 10.1016/j.cognition.2008.12.009

[2] ↑ Thomson, D. R., Besner, D., and Smilek, D. 2015. A resource-control account of sustained attention: evidence from mind-wandering and vigilance paradigms. Perspect. Psychol. Sci . 10:82–96. doi: 10.1177/1745691614556681

[3] ↑ Smallwood, J., and Schooler, J. W. 2006. The restless mind. Psychol. Bull . 132:946–58. doi: 10.1037/0033-2909.132.6.946

[4] ↑ Cheyne, J. A., Carriere, J. S. A., and Smilek, D. 2009. Absent minds and absent agents: attention-lapse induced alienation of agency. Conscious Cogn . 18:481–93. doi: 10.1016/j.concog.2009.01.005

[5] ↑ Blondé, P., Girardeau, J. C., Sperduti, M., and Piolino, P. 2022. A wandering mind is a forgetful mind: a systematic review on the influence of mind wandering on episodic memory encoding. Neurosci. Biobehav. Rev . 132:774–92. doi: 10.1016/j.neubiorev.2021.11.015

[6] ↑ Ralph, B. C. W., Smith, A. C., Seli, P., and Smilek, D. 2019. Yearning for distraction: evidence for a trade-off between media multitasking and mind wandering. Can. J. Exp. Psychol . 74:56–72. doi: 10.1037/cep0000186

[7] ↑ Mowlem, F. D., Skirrow, C., Reid, P., Maltezos, S., Nijjar, S. K., Merwood, A., et al. 2019. Validation of the mind excessively wandering scale and the relationship of mind wandering to impairment in adult ADHD. J. Atten. Disord . 23:624–34. doi: 10.1177/1087054716651927

[8] ↑ Schooler, J. W., Smallwood, J., Christoff, K., Handy, T. C., Reichle, E. D., and Sayette, M. A. 2011. Meta-awareness, perceptual decoupling and the wandering mind. Trends Cogn. Sci. 15:319–26. doi: 10.1016/j.tics.2011.05.006

[9] ↑ Decker, A., Dubois, M., Duncan, K., and Finn, A. S. 2022. Pay attention and you might miss it: greater learning during attentional lapses. Psychon. Bull. Rev . 30:1041–52. doi: 10.3758/s13423-022-02226-6

[10] ↑ Litman, L., and Davachi, L. 2008. Distributed learning enhances relational memory consolidation. Learn Mem . 15:711–6. doi: 10.1101/lm.1132008

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6 Books for Adults Living With A.D.H.D.

Psychiatrists, counselors and researchers shared their recommendations.

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Understanding A.D.H.D.

The challenges faced by those with attention deficit hyperactivity disorder can be daunting. but people who are diagnosed with it can still thrive..

Millions of children in the United States have received a diagnosis of A.D.H.D . Here is how their families can support them .

The condition is also being recognized more in adults . These are some of the behaviors  that might be associated with adult A.D.H.D.

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Everyone has bouts of distraction and forgetfulness. Here is when psychiatrists diagnose it as something clinical .

The disorder can put a strain on relationships. But there are ways to cope .

Though meditation can be beneficial to those with A.D.H.D., sitting still and focusing on breathing can be hard for them. These tips can help .

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